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[7.8][ML] Fix for SIGSEGV forecasting #1405

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Merged
merged 1 commit into from
Jul 14, 2020
Merged

[7.8][ML] Fix for SIGSEGV forecasting #1405

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1 change: 1 addition & 0 deletions docs/CHANGELOG.asciidoc
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
=== Bug Fixes

* Fix restoration of change detectors after seasonality change. (See {ml-pull}1391[#1391].)
* Fix potential SIGSEGV when forecasting. (See {ml-pull}1402[#1402], issue: {ml-issue}1401[#1401].)

== {es} version 7.8.1

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27 changes: 13 additions & 14 deletions lib/model/CAnomalyDetector.cc
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Expand Up @@ -499,14 +499,14 @@ CAnomalyDetector::getForecastModels(bool persistOnDisk,
return series;
}

TModelDetailsViewUPtr view = m_Model.get()->details();
TModelDetailsViewUPtr view{m_Model.get()->details()};

// The view can be empty, e.g. for the counting model.
if (view.get() == nullptr) {
return series;
}

const CSearchKey& key = m_DataGatherer->searchKey();
const CSearchKey& key{m_DataGatherer->searchKey()};
series.s_ByFieldName = key.byFieldName();
series.s_DetectorIndex = m_DetectorIndex;
series.s_PartitionFieldName = key.partitionFieldName();
Expand All @@ -516,16 +516,15 @@ CAnomalyDetector::getForecastModels(bool persistOnDisk,
if (persistOnDisk) {
CForecastModelPersist::CPersist persister(persistenceFolder);

for (std::size_t pid = 0u, maxPid = m_DataGatherer->numberPeople();
pid < maxPid; ++pid) {
for (std::size_t pid = 0; pid < m_DataGatherer->numberPeople(); ++pid) {
// todo: Add terms filtering here
if (m_DataGatherer->isPersonActive(pid)) {
for (auto feature : view->features()) {
const maths::CModel* model = view->model(feature, pid);
core_t::TTime firstDataTime;
core_t::TTime lastDataTime;
std::tie(firstDataTime, lastDataTime) = view->dataTimeInterval(pid);
const maths::CModel* model{view->model(feature, pid)};
if (model != nullptr && model->isForecastPossible()) {
core_t::TTime firstDataTime;
core_t::TTime lastDataTime;
std::tie(firstDataTime, lastDataTime) = view->dataTimeInterval(pid);
persister.addModel(model, firstDataTime, lastDataTime, feature,
m_DataGatherer->personName(pid));
}
Expand All @@ -535,16 +534,16 @@ CAnomalyDetector::getForecastModels(bool persistOnDisk,

series.s_ToForecastPersisted = persister.finalizePersistAndGetFile();
} else {
for (std::size_t pid = 0u, maxPid = m_DataGatherer->numberPeople();
pid < maxPid; ++pid) {

for (std::size_t pid = 0; pid < m_DataGatherer->numberPeople(); ++pid) {
// todo: Add terms filtering here
if (m_DataGatherer->isPersonActive(pid)) {
for (auto feature : view->features()) {
const maths::CModel* model = view->model(feature, pid);
core_t::TTime firstDataTime;
core_t::TTime lastDataTime;
std::tie(firstDataTime, lastDataTime) = view->dataTimeInterval(pid);
const maths::CModel* model{view->model(feature, pid)};
if (model != nullptr && model->isForecastPossible()) {
core_t::TTime firstDataTime;
core_t::TTime lastDataTime;
std::tie(firstDataTime, lastDataTime) = view->dataTimeInterval(pid);
series.s_ToForecast.emplace_back(
feature, m_DataGatherer->personName(pid),
CForecastDataSink::TMathsModelPtr(model->cloneForForecast()),
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