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[ML] Speed up trend model component prediction #73

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Merged
tveasey merged 2 commits into from
Apr 26, 2018
Merged

[ML] Speed up trend model component prediction #73

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cbaab3b
Don't compute predictions from trend models which have low weight for…
tveasey Apr 19, 2018
ee424c9
Merge branch 'master' into enhancement/speedup-trend-evaluation
tveasey Apr 24, 2018
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16 changes: 11 additions & 5 deletions lib/maths/CTrendComponent.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,7 @@ const std::string RESIDUAL_MOMENTS_TAG{"c"};
const double TIME_SCALES[]{144.0, 72.0, 36.0, 12.0, 4.0, 1.0, 0.25, 0.05};
const std::size_t NUMBER_MODELS{boost::size(TIME_SCALES)};
const double MAX_CONDITION{1e12};
const double MINIMUM_WEIGHT_TO_USE_MODEL_FOR_PREDICTION{0.01};
const core_t::TTime UNSET_TIME{0};
}

Expand Down Expand Up @@ -210,11 +211,16 @@ CTrendComponent::TDoubleDoublePr CTrendComponent::value(core_t::TTime time,
double scaledTime{scaleTime(time, m_RegressionOrigin)};

TMeanAccumulator prediction_;
{
TDoubleVec factors(this->factors(std::abs(time - m_LastUpdate)));
for (std::size_t i = 0u; i < NUMBER_MODELS; ++i) {
prediction_.add(m_Models[i].s_Regression.predict(scaledTime, MAX_CONDITION),
factors[i] * CBasicStatistics::mean(m_Models[i].s_Weight));

TDoubleVec weights(this->factors(std::abs(time - m_LastUpdate)));
double Z{0.0};
for (std::size_t i = 0u; i < NUMBER_MODELS; ++i) {
weights[i] *= CBasicStatistics::mean(m_Models[i].s_Weight);
Z += weights[i];
}
for (std::size_t i = 0u; i < NUMBER_MODELS; ++i) {
if (weights[i] > MINIMUM_WEIGHT_TO_USE_MODEL_FOR_PREDICTION * Z) {
prediction_.add(m_Models[i].s_Regression.predict(scaledTime, MAX_CONDITION), weights[i]);
}
}

Expand Down
8 changes: 4 additions & 4 deletions lib/maths/unittest/CForecastTest.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -90,7 +90,7 @@ void CForecastTest::testDailyNoLongTermTrend() {
return 40.0 + alpha * y[i / 6] + beta * y[(i / 6 + 1) % y.size()] + noise;
};

this->test(trend, bucketLength, 60, 64.0, 4.0, 0.13);
this->test(trend, bucketLength, 60, 64.0, 5.0, 0.14);
}

void CForecastTest::testDailyConstantLongTermTrend() {
Expand Down Expand Up @@ -163,7 +163,7 @@ void CForecastTest::testComplexConstantLongTermTrend() {
scale[d] * (20.0 + y[h] + noise);
};

this->test(trend, bucketLength, 60, 24.0, 17.0, 0.04);
this->test(trend, bucketLength, 60, 24.0, 13.0, 0.01);
}

void CForecastTest::testComplexVaryingLongTermTrend() {
Expand Down Expand Up @@ -193,7 +193,7 @@ void CForecastTest::testComplexVaryingLongTermTrend() {
return trend_.value(time_) + scale[d] * (20.0 + y[h] + noise);
};

this->test(trend, bucketLength, 60, 4.0, 23.0, 0.05);
this->test(trend, bucketLength, 60, 4.0, 24.0, 0.051);
}

void CForecastTest::testNonNegative() {
Expand Down Expand Up @@ -363,7 +363,7 @@ void CForecastTest::testFinancialIndex() {
//file << "my = " << core::CContainerPrinter::print(my) << ";\n";
//file << "uy = " << core::CContainerPrinter::print(uy) << ";\n";

CPPUNIT_ASSERT(percentageOutOfBounds < 53.0);
CPPUNIT_ASSERT(percentageOutOfBounds < 52.0);
CPPUNIT_ASSERT(maths::CBasicStatistics::mean(error) < 0.1);
}

Expand Down