summaryrefslogtreecommitdiffstats
path: root/ml/Dimension.h
diff options
context:
space:
mode:
Diffstat (limited to 'ml/Dimension.h')
-rw-r--r--ml/Dimension.h198
1 files changed, 0 insertions, 198 deletions
diff --git a/ml/Dimension.h b/ml/Dimension.h
deleted file mode 100644
index 2b1adfff9..000000000
--- a/ml/Dimension.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef ML_DIMENSION_H
-#define ML_DIMENSION_H
-
-#include "Mutex.h"
-#include "Stats.h"
-#include "Query.h"
-#include "Config.h"
-
-#include "ml-private.h"
-
-namespace ml {
-
-static inline std::string getMLDimensionID(RRDDIM *RD) {
- RRDSET *RS = RD->rrdset;
-
- std::stringstream SS;
- SS << rrdset_context(RS) << "|" << rrdset_id(RS) << "|" << rrddim_name(RD);
- return SS.str();
-}
-
-enum class MachineLearningStatus {
- // Enable training/prediction
- Enabled,
-
- // Disable due to update every being different from the host's
- DisabledDueToUniqueUpdateEvery,
-
- // Disable because configuration pattern matches the chart's id
- DisabledDueToExcludedChart,
-};
-
-enum class TrainingStatus {
- // We don't have a model for this dimension
- Untrained,
-
- // Request for training sent, but we don't have any models yet
- PendingWithoutModel,
-
- // Request to update existing models sent
- PendingWithModel,
-
- // Have a valid, up-to-date model
- Trained,
-};
-
-enum class MetricType {
- // The dimension has constant values, no need to train
- Constant,
-
- // The dimension's values fluctuate, we need to generate a model
- Variable,
-};
-
-struct TrainingRequest {
- // Chart/dimension we want to train
- STRING *ChartId;
- STRING *DimensionId;
-
- // Creation time of request
- time_t RequestTime;
-
- // First/last entry of this dimension in DB
- // at the point the request was made
- time_t FirstEntryOnRequest;
- time_t LastEntryOnRequest;
-};
-
-void dumpTrainingRequest(const TrainingRequest &TrainingReq, const char *Prefix);
-
-enum TrainingResult {
- // We managed to create a KMeans model
- Ok,
- // Could not query DB with a correct time range
- InvalidQueryTimeRange,
- // Did not gather enough data from DB to run KMeans
- NotEnoughCollectedValues,
- // Acquired a null dimension
- NullAcquiredDimension,
- // Chart is under replication
- ChartUnderReplication,
-};
-
-struct TrainingResponse {
- // Time when the request for this response was made
- time_t RequestTime;
-
- // First/last entry of the dimension in DB when generating the request
- time_t FirstEntryOnRequest;
- time_t LastEntryOnRequest;
-
- // First/last entry of the dimension in DB when generating the response
- time_t FirstEntryOnResponse;
- time_t LastEntryOnResponse;
-
- // After/Before timestamps of our DB query
- time_t QueryAfterT;
- time_t QueryBeforeT;
-
- // Actual after/before returned by the DB query ops
- time_t DbAfterT;
- time_t DbBeforeT;
-
- // Number of doubles returned by the DB query
- size_t CollectedValues;
-
- // Number of values we return to the caller
- size_t TotalValues;
-
- // Result of training response
- TrainingResult Result;
-};
-
-void dumpTrainingResponse(const TrainingResponse &TrainingResp, const char *Prefix);
-
-class Dimension {
-public:
- Dimension(RRDDIM *RD) :
- RD(RD),
- MT(MetricType::Constant),
- TS(TrainingStatus::Untrained),
- TR(),
- LastTrainingTime(0)
- {
- if (simple_pattern_matches(Cfg.SP_ChartsToSkip, rrdset_name(RD->rrdset)))
- MLS = MachineLearningStatus::DisabledDueToExcludedChart;
- else if (RD->update_every != RD->rrdset->rrdhost->rrd_update_every)
- MLS = MachineLearningStatus::DisabledDueToUniqueUpdateEvery;
- else
- MLS = MachineLearningStatus::Enabled;
-
- Models.reserve(Cfg.NumModelsToUse);
- }
-
- RRDDIM *getRD() const {
- return RD;
- }
-
- unsigned updateEvery() const {
- return RD->update_every;
- }
-
- MetricType getMT() const {
- return MT;
- }
-
- TrainingStatus getTS() const {
- return TS;
- }
-
- MachineLearningStatus getMLS() const {
- return MLS;
- }
-
- TrainingResult trainModel(const TrainingRequest &TR);
-
- void scheduleForTraining(time_t CurrT);
-
- bool predict(time_t CurrT, CalculatedNumber Value, bool Exists);
-
- std::vector<KMeans> getModels();
-
- void dump() const;
-
-private:
- TrainingRequest getTrainingRequest(time_t CurrT) const {
- return TrainingRequest {
- string_dup(RD->rrdset->id),
- string_dup(RD->id),
- CurrT,
- rrddim_first_entry_s(RD),
- rrddim_last_entry_s(RD)
- };
- }
-
-private:
- std::pair<CalculatedNumber *, TrainingResponse> getCalculatedNumbers(const TrainingRequest &TrainingReq);
-
-public:
- RRDDIM *RD;
- MetricType MT;
- TrainingStatus TS;
- TrainingResponse TR;
-
- time_t LastTrainingTime;
-
- MachineLearningStatus MLS;
-
- std::vector<CalculatedNumber> CNs;
- DSample Feature;
- std::vector<KMeans> Models;
- Mutex M;
-};
-
-} // namespace ml
-
-#endif /* ML_DIMENSION_H */