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// SPDX-License-Identifier: GPL-3.0-or-later
#include "Config.h"
#include "ml-private.h"
using namespace ml;
/*
* Global configuration instance to be shared between training and
* prediction threads.
*/
Config ml::Cfg;
template <typename T>
static T clamp(const T& Value, const T& Min, const T& Max) {
return std::max(Min, std::min(Value, Max));
}
/*
* Initialize global configuration variable.
*/
void Config::readMLConfig(void) {
const char *ConfigSectionML = CONFIG_SECTION_ML;
bool EnableAnomalyDetection = config_get_boolean(ConfigSectionML, "enabled", false);
/*
* Read values
*/
unsigned MaxTrainSamples = config_get_number(ConfigSectionML, "maximum num samples to train", 4 * 3600);
unsigned MinTrainSamples = config_get_number(ConfigSectionML, "minimum num samples to train", 1 * 3600);
unsigned TrainEvery = config_get_number(ConfigSectionML, "train every", 1 * 3600);
unsigned DiffN = config_get_number(ConfigSectionML, "num samples to diff", 1);
unsigned SmoothN = config_get_number(ConfigSectionML, "num samples to smooth", 3);
unsigned LagN = config_get_number(ConfigSectionML, "num samples to lag", 5);
unsigned MaxKMeansIters = config_get_number(ConfigSectionML, "maximum number of k-means iterations", 1000);
double DimensionAnomalyScoreThreshold = config_get_float(ConfigSectionML, "dimension anomaly score threshold", 0.99);
double HostAnomalyRateThreshold = config_get_float(ConfigSectionML, "host anomaly rate threshold", 0.01);
double ADMinWindowSize = config_get_float(ConfigSectionML, "minimum window size", 30);
double ADMaxWindowSize = config_get_float(ConfigSectionML, "maximum window size", 600);
double ADIdleWindowSize = config_get_float(ConfigSectionML, "idle window size", 30);
double ADWindowRateThreshold = config_get_float(ConfigSectionML, "window minimum anomaly rate", 0.25);
double ADDimensionRateThreshold = config_get_float(ConfigSectionML, "anomaly event min dimension rate threshold", 0.05);
std::stringstream SS;
SS << netdata_configured_cache_dir << "/anomaly-detection.db";
Cfg.AnomalyDBPath = SS.str();
/*
* Clamp
*/
MaxTrainSamples = clamp(MaxTrainSamples, 1 * 3600u, 6 * 3600u);
MinTrainSamples = clamp(MinTrainSamples, 1 * 3600u, 6 * 3600u);
TrainEvery = clamp(TrainEvery, 1 * 3600u, 6 * 3600u);
DiffN = clamp(DiffN, 0u, 1u);
SmoothN = clamp(SmoothN, 0u, 5u);
LagN = clamp(LagN, 0u, 5u);
MaxKMeansIters = clamp(MaxKMeansIters, 500u, 1000u);
DimensionAnomalyScoreThreshold = clamp(DimensionAnomalyScoreThreshold, 0.01, 5.00);
HostAnomalyRateThreshold = clamp(HostAnomalyRateThreshold, 0.01, 1.0);
ADMinWindowSize = clamp(ADMinWindowSize, 30.0, 300.0);
ADMaxWindowSize = clamp(ADMaxWindowSize, 60.0, 900.0);
ADIdleWindowSize = clamp(ADIdleWindowSize, 30.0, 900.0);
ADWindowRateThreshold = clamp(ADWindowRateThreshold, 0.01, 0.99);
ADDimensionRateThreshold = clamp(ADDimensionRateThreshold, 0.01, 0.99);
/*
* Validate
*/
if (MinTrainSamples >= MaxTrainSamples) {
error("invalid min/max train samples found (%u >= %u)", MinTrainSamples, MaxTrainSamples);
MinTrainSamples = 1 * 3600;
MaxTrainSamples = 4 * 3600;
}
if (ADMinWindowSize >= ADMaxWindowSize) {
error("invalid min/max anomaly window size found (%lf >= %lf)", ADMinWindowSize, ADMaxWindowSize);
ADMinWindowSize = 30.0;
ADMaxWindowSize = 600.0;
}
/*
* Assign to config instance
*/
Cfg.EnableAnomalyDetection = EnableAnomalyDetection;
Cfg.MaxTrainSamples = MaxTrainSamples;
Cfg.MinTrainSamples = MinTrainSamples;
Cfg.TrainEvery = TrainEvery;
Cfg.DiffN = DiffN;
Cfg.SmoothN = SmoothN;
Cfg.LagN = LagN;
Cfg.MaxKMeansIters = MaxKMeansIters;
Cfg.DimensionAnomalyScoreThreshold = DimensionAnomalyScoreThreshold;
Cfg.HostAnomalyRateThreshold = HostAnomalyRateThreshold;
Cfg.ADMinWindowSize = ADMinWindowSize;
Cfg.ADMaxWindowSize = ADMaxWindowSize;
Cfg.ADIdleWindowSize = ADIdleWindowSize;
Cfg.ADWindowRateThreshold = ADWindowRateThreshold;
Cfg.ADDimensionRateThreshold = ADDimensionRateThreshold;
Cfg.HostsToSkip = config_get(ConfigSectionML, "hosts to skip from training", "!*");
Cfg.SP_HostsToSkip = simple_pattern_create(Cfg.HostsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
Cfg.ChartsToSkip = config_get(ConfigSectionML, "charts to skip from training",
"!system.* !cpu.* !mem.* !disk.* !disk_* "
"!ip.* !ipv4.* !ipv6.* !net.* !net_* !netfilter.* "
"!services.* !apps.* !groups.* !user.* !ebpf.* !netdata.* *");
Cfg.SP_ChartsToSkip = simple_pattern_create(ChartsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
}
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