[ML] Fix sparse data edge cases for periodicity testing (#28)

This fixes issue #20. Digging into the root cause, they were all down to very sparse data over the 
window we maintain to test for periodicity. This showed up the need to lower bound the count of 
buckets with periodic repeats when testing for periodic partitions.

Author: tveasey

include/maths/CPeriodicityHypothesisTests.h

@@ -344,14 +344,15 @@ class MATHS_EXPORT CPeriodicityHypothesisTests
             testForDailyWithWeekend(const TFloatMeanAccumulatorCRng &buckets,
                                     STestStats &stats) const;
 
-        //! Test for a weekday/end partition with weekly .
+        //! Test for a weekly period given we think there is a
+        //! weekday/end partition.
         CPeriodicityHypothesisTestsResult
             testForWeeklyGivenDailyWithWeekend(const TTimeTimePr2Vec &window,
                                                const TFloatMeanAccumulatorCRng &buckets,
                                                STestStats &stats) const;
 
-        //! Test for the specified period given we think there is
-        //! some diurnal periodicity.
+        //! Test for the specified period given we think there is diurnal
+        //! periodicity.
         CPeriodicityHypothesisTestsResult
             testForPeriod(const TTimeTimePr2Vec &window,
                           const TFloatMeanAccumulatorCRng &buckets,
@@ -361,6 +362,11 @@ class MATHS_EXPORT CPeriodicityHypothesisTests
         bool seenSufficientDataToTest(core_t::TTime period,
                                       const TFloatMeanAccumulatorCRng &buckets) const;
 
+        //! Check if there are enough non-empty buckets which are repeated
+        //! at at least one \p period in \p buckets.
+        bool seenSufficientPeriodicallyPopulatedBucketsToTest(const TFloatMeanAccumulatorCRng &buckets,
+                                                              std::size_t period) const;
+
         //! Compute various ancillary statistics for testing.
         bool testStatisticsFor(const TFloatMeanAccumulatorCRng &buckets,
                                STestStats &stats) const;

lib/maths/CPeriodicityHypothesisTests.cc

@@ -1039,18 +1039,18 @@ CPeriodicityHypothesisTests::best(const TNestedHypothesesVec &hypotheses) const
     {
         STestStats stats;
         CPeriodicityHypothesisTestsResult resultForHypothesis{hypothesis.test(stats)};
-        summaries.emplace_back(stats.s_V0, stats.s_B - stats.s_DF0,
-                               std::move(resultForHypothesis));
+        if (stats.s_B > stats.s_DF0)
+        {
+            summaries.emplace_back(stats.s_V0, stats.s_B - stats.s_DF0,
+                                   std::move(resultForHypothesis));
+        }
     }
 
     TMinAccumulator vCutoff;
     for (const auto &summary : summaries)
     {
-        if (summary.s_DF > 0.0)
-        {
-            vCutoff.add(varianceAtPercentile(summary.s_V, summary.s_DF,
-                                             50.0 + CONFIDENCE_INTERVAL / 2.0));
-        }
+        vCutoff.add(varianceAtPercentile(summary.s_V, summary.s_DF,
+                                         50.0 + CONFIDENCE_INTERVAL / 2.0));
     }
     if (vCutoff.count() > 0)
     {
@@ -1284,6 +1284,26 @@ bool CPeriodicityHypothesisTests::seenSufficientDataToTest(core_t::TTime period,
                  >= 2.0 * ACCURATE_TEST_POPULATED_FRACTION * static_cast<double>(period);
 }
 
+bool CPeriodicityHypothesisTests::seenSufficientPeriodicallyPopulatedBucketsToTest(const TFloatMeanAccumulatorCRng &buckets,
+                                                                                   std::size_t period) const
+{
+    double repeats{0.0};
+    for (std::size_t i = 0u; i < period; ++i)
+    {
+        for (std::size_t j = i + period; j < buckets.size(); j += period)
+        {
+            if (  CBasicStatistics::count(buckets[j])
+                * CBasicStatistics::count(buckets[j - period]) > 0.0)
+            {
+                repeats += 1.0;
+                break;
+            }
+        }
+    }
+    LOG_TRACE("repeated values = " << repeats);
+    return repeats >= static_cast<double>(period) * ACCURATE_TEST_POPULATED_FRACTION / 3.0;
+}
+
 bool CPeriodicityHypothesisTests::testStatisticsFor(const TFloatMeanAccumulatorCRng &buckets,
                                                     STestStats &stats) const
 {
@@ -1433,21 +1453,7 @@ bool CPeriodicityHypothesisTests::testPeriod(const TTimeTimePr2Vec &windows,
 
     // We need to observe a minimum number of repeated values to test with
     // an acceptable false positive rate.
-    double repeats{0.0};
-    for (std::size_t i = 0u; i < period; ++i)
-    {
-        for (std::size_t j = i + period; j < buckets.size(); j += period)
-        {
-            if (  CBasicStatistics::count(buckets[j])
-                * CBasicStatistics::count(buckets[j - period]) > 0.0)
-            {
-                repeats += 1.0;
-                break;
-            }
-        }
-    }
-    LOG_TRACE("  repeated values = " << repeats);
-    if (repeats < static_cast<double>(period) * ACCURATE_TEST_POPULATED_FRACTION / 3.0)
+    if (!this->seenSufficientPeriodicallyPopulatedBucketsToTest(buckets, period))
     {
         return false;
     }
@@ -1493,7 +1499,8 @@ bool CPeriodicityHypothesisTests::testPeriod(const TTimeTimePr2Vec &windows,
         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)
+        if (   v1 < vt && B > 1.0
+            && CStatisticalTests::leftTailFTest(v1 / v0, df1, df0) <= MAXIMUM_SIGNIFICANCE)
         {
             double R{CSignal::autocorrelation(period, values)};
             R = autocorrelationAtPercentile(R, B, 50.0 - CONFIDENCE_INTERVAL / 2.0);
@@ -1567,6 +1574,15 @@ bool CPeriodicityHypothesisTests::testPartition(const TTimeTimePr2Vec &partition
     {
         return false;
     }
+
+    std::size_t period{static_cast<std::size_t>(period_ / m_BucketLength)};
+
+    // We need to observe a minimum number of repeated values to test with
+    // an acceptable false positive rate.
+    if (!this->seenSufficientPeriodicallyPopulatedBucketsToTest(buckets, period))
+    {
+        return false;
+    }
     if (stats.s_HasPartition)
     {
         return true;
@@ -1577,7 +1593,6 @@ bool CPeriodicityHypothesisTests::testPartition(const TTimeTimePr2Vec &partition
     // evidence that it reduces the residual variance and repeats.
 
     core_t::TTime windowLength{length(buckets, m_BucketLength)};
-    std::size_t period{static_cast<std::size_t>(period_ / m_BucketLength)};
     core_t::TTime repeat{length(partition)};
     core_t::TTime startOfPartition{stats.s_StartOfPartition};
     double B{stats.s_B};
@@ -1732,11 +1747,13 @@ bool CPeriodicityHypothesisTests::testPartition(const TTimeTimePr2Vec &partition
                 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 (BW > 1.0)
+                {
+                    double RW{CSignal::autocorrelation(windowLength_ + period, partitionValues)};
+                    R = std::max(R, autocorrelationAtPercentile(RW, BW, 50.0 - CONFIDENCE_INTERVAL / 2.0));
+                    LOG_TRACE("  autocorrelation          = " << R);
+                    LOG_TRACE("  autocorrelationThreshold = " << Rt);
+                }
             }
 
             if (R > Rt)