Adding upstream version 1.34.0.upstream/1.34.0

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 15 insertions, 5 deletions

diff --git a/ml/Config.cc b/ml/Config.cc
index df0a2825..99109e05 100644
--- a/ml/Config.cc
+++ b/ml/Config.cc
@@ -32,10 +32,13 @@ void Config::readMLConfig(void) {
     unsigned MinTrainSamples = config_get_number(ConfigSectionML, "minimum num samples to train", 1 * 3600);
     unsigned TrainEvery = config_get_number(ConfigSectionML, "train every", 1 * 3600);
 
+    unsigned DBEngineAnomalyRateEvery = config_get_number(ConfigSectionML, "dbengine anomaly rate every", 60);
+
     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);
 
+    double RandomSamplingRatio = config_get_float(ConfigSectionML, "random sampling ratio", 1.0 / LagN);
     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);
@@ -59,10 +62,13 @@ void Config::readMLConfig(void) {
     MinTrainSamples = clamp(MinTrainSamples, 1 * 3600u, 6 * 3600u);
     TrainEvery = clamp(TrainEvery, 1 * 3600u, 6 * 3600u);
 
+    DBEngineAnomalyRateEvery = clamp(DBEngineAnomalyRateEvery, 1 * 30u, 15 * 60u);
+
     DiffN = clamp(DiffN, 0u, 1u);
     SmoothN = clamp(SmoothN, 0u, 5u);
-    LagN = clamp(LagN, 0u, 5u);
+    LagN = clamp(LagN, 1u, 5u);
 
+    RandomSamplingRatio = clamp(RandomSamplingRatio, 0.2, 1.0);
     MaxKMeansIters = clamp(MaxKMeansIters, 500u, 1000u);
 
     DimensionAnomalyScoreThreshold = clamp(DimensionAnomalyScoreThreshold, 0.01, 5.00);
@@ -102,10 +108,13 @@ void Config::readMLConfig(void) {
     Cfg.MinTrainSamples = MinTrainSamples;
     Cfg.TrainEvery = TrainEvery;
 
+    Cfg.DBEngineAnomalyRateEvery = DBEngineAnomalyRateEvery;
+
     Cfg.DiffN = DiffN;
     Cfg.SmoothN = SmoothN;
     Cfg.LagN = LagN;
 
+    Cfg.RandomSamplingRatio = RandomSamplingRatio;
     Cfg.MaxKMeansIters = MaxKMeansIters;
 
     Cfg.DimensionAnomalyScoreThreshold = DimensionAnomalyScoreThreshold;
@@ -120,9 +129,10 @@ void Config::readMLConfig(void) {
     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.* *");
+    // Always exclude anomaly_detection charts from training.
+    Cfg.ChartsToSkip = "anomaly_detection.* ";
+    Cfg.ChartsToSkip += config_get(ConfigSectionML, "charts to skip from training", "netdata.*");
     Cfg.SP_ChartsToSkip = simple_pattern_create(ChartsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
+
+    Cfg.StreamADCharts = config_get_boolean(ConfigSectionML, "stream anomaly detection charts", true);
 }