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-rw-r--r--ml/Config.cc123
1 files changed, 67 insertions, 56 deletions
diff --git a/ml/Config.cc b/ml/Config.cc
index ba3a6144..d451c602 100644
--- a/ml/Config.cc
+++ b/ml/Config.cc
@@ -1,15 +1,12 @@
// 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;
+ml_config_t Cfg;
template <typename T>
static T clamp(const T& Value, const T& Min, const T& Max) {
@@ -19,96 +16,110 @@ static T clamp(const T& Value, const T& Min, const T& Max) {
/*
* Initialize global configuration variable.
*/
-void Config::readMLConfig(void) {
- const char *ConfigSectionML = CONFIG_SECTION_ML;
+void ml_config_load(ml_config_t *cfg) {
+ const char *config_section_ml = CONFIG_SECTION_ML;
- bool EnableAnomalyDetection = config_get_boolean(ConfigSectionML, "enabled", true);
+ bool enable_anomaly_detection = config_get_boolean(config_section_ml, "enabled", true);
/*
* 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 * 900);
- unsigned TrainEvery = config_get_number(ConfigSectionML, "train every", 1 * 3600);
- unsigned NumModelsToUse = config_get_number(ConfigSectionML, "number of models per dimension", 1);
+ unsigned max_train_samples = config_get_number(config_section_ml, "maximum num samples to train", 4 * 3600);
+ unsigned min_train_samples = config_get_number(config_section_ml, "minimum num samples to train", 1 * 900);
+ unsigned train_every = config_get_number(config_section_ml, "train every", 1 * 3600);
+ unsigned num_models_to_use = config_get_number(config_section_ml, "number of models per dimension", 1);
+
+ unsigned diff_n = config_get_number(config_section_ml, "num samples to diff", 1);
+ unsigned smooth_n = config_get_number(config_section_ml, "num samples to smooth", 3);
+ unsigned lag_n = config_get_number(config_section_ml, "num samples to lag", 5);
+
+ double random_sampling_ratio = config_get_float(config_section_ml, "random sampling ratio", 1.0 / 5.0 /* default lag_n */);
+ unsigned max_kmeans_iters = config_get_number(config_section_ml, "maximum number of k-means iterations", 1000);
- 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 dimension_anomaly_rate_threshold = config_get_float(config_section_ml, "dimension anomaly score threshold", 0.99);
- 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 host_anomaly_rate_threshold = config_get_float(config_section_ml, "host anomaly rate threshold", 1.0);
+ std::string anomaly_detection_grouping_method = config_get(config_section_ml, "anomaly detection grouping method", "average");
+ time_t anomaly_detection_query_duration = config_get_number(config_section_ml, "anomaly detection grouping duration", 5 * 60);
- double DimensionAnomalyScoreThreshold = config_get_float(ConfigSectionML, "dimension anomaly score threshold", 0.99);
+ size_t num_training_threads = config_get_number(config_section_ml, "num training threads", 4);
+ size_t flush_models_batch_size = config_get_number(config_section_ml, "flush models batch size", 128);
- double HostAnomalyRateThreshold = config_get_float(ConfigSectionML, "host anomaly rate threshold", 1.0);
- std::string AnomalyDetectionGroupingMethod = config_get(ConfigSectionML, "anomaly detection grouping method", "average");
- time_t AnomalyDetectionQueryDuration = config_get_number(ConfigSectionML, "anomaly detection grouping duration", 5 * 60);
+ bool enable_statistics_charts = config_get_boolean(config_section_ml, "enable statistics charts", true);
/*
* Clamp
*/
- MaxTrainSamples = clamp<unsigned>(MaxTrainSamples, 1 * 3600, 24 * 3600);
- MinTrainSamples = clamp<unsigned>(MinTrainSamples, 1 * 900, 6 * 3600);
- TrainEvery = clamp<unsigned>(TrainEvery, 1 * 3600, 6 * 3600);
- NumModelsToUse = clamp<unsigned>(NumModelsToUse, 1, 7 * 24);
+ max_train_samples = clamp<unsigned>(max_train_samples, 1 * 3600, 24 * 3600);
+ min_train_samples = clamp<unsigned>(min_train_samples, 1 * 900, 6 * 3600);
+ train_every = clamp<unsigned>(train_every, 1 * 3600, 6 * 3600);
+ num_models_to_use = clamp<unsigned>(num_models_to_use, 1, 7 * 24);
- DiffN = clamp(DiffN, 0u, 1u);
- SmoothN = clamp(SmoothN, 0u, 5u);
- LagN = clamp(LagN, 1u, 5u);
+ diff_n = clamp(diff_n, 0u, 1u);
+ smooth_n = clamp(smooth_n, 0u, 5u);
+ lag_n = clamp(lag_n, 1u, 5u);
- RandomSamplingRatio = clamp(RandomSamplingRatio, 0.2, 1.0);
- MaxKMeansIters = clamp(MaxKMeansIters, 500u, 1000u);
+ random_sampling_ratio = clamp(random_sampling_ratio, 0.2, 1.0);
+ max_kmeans_iters = clamp(max_kmeans_iters, 500u, 1000u);
- DimensionAnomalyScoreThreshold = clamp(DimensionAnomalyScoreThreshold, 0.01, 5.00);
+ dimension_anomaly_rate_threshold = clamp(dimension_anomaly_rate_threshold, 0.01, 5.00);
- HostAnomalyRateThreshold = clamp(HostAnomalyRateThreshold, 0.1, 10.0);
- AnomalyDetectionQueryDuration = clamp<time_t>(AnomalyDetectionQueryDuration, 60, 15 * 60);
+ host_anomaly_rate_threshold = clamp(host_anomaly_rate_threshold, 0.1, 10.0);
+ anomaly_detection_query_duration = clamp<time_t>(anomaly_detection_query_duration, 60, 15 * 60);
+
+ num_training_threads = clamp<size_t>(num_training_threads, 1, 128);
+ flush_models_batch_size = clamp<size_t>(flush_models_batch_size, 8, 512);
/*
* Validate
*/
- if (MinTrainSamples >= MaxTrainSamples) {
- error("invalid min/max train samples found (%u >= %u)", MinTrainSamples, MaxTrainSamples);
+ if (min_train_samples >= max_train_samples) {
+ error("invalid min/max train samples found (%u >= %u)", min_train_samples, max_train_samples);
- MinTrainSamples = 1 * 3600;
- MaxTrainSamples = 4 * 3600;
+ min_train_samples = 1 * 3600;
+ max_train_samples = 4 * 3600;
}
/*
* Assign to config instance
*/
- Cfg.EnableAnomalyDetection = EnableAnomalyDetection;
+ cfg->enable_anomaly_detection = enable_anomaly_detection;
- Cfg.MaxTrainSamples = MaxTrainSamples;
- Cfg.MinTrainSamples = MinTrainSamples;
- Cfg.TrainEvery = TrainEvery;
- Cfg.NumModelsToUse = NumModelsToUse;
+ cfg->max_train_samples = max_train_samples;
+ cfg->min_train_samples = min_train_samples;
+ cfg->train_every = train_every;
- Cfg.DiffN = DiffN;
- Cfg.SmoothN = SmoothN;
- Cfg.LagN = LagN;
+ cfg->num_models_to_use = num_models_to_use;
- Cfg.RandomSamplingRatio = RandomSamplingRatio;
- Cfg.MaxKMeansIters = MaxKMeansIters;
+ cfg->diff_n = diff_n;
+ cfg->smooth_n = smooth_n;
+ cfg->lag_n = lag_n;
- Cfg.DimensionAnomalyScoreThreshold = DimensionAnomalyScoreThreshold;
+ cfg->random_sampling_ratio = random_sampling_ratio;
+ cfg->max_kmeans_iters = max_kmeans_iters;
- Cfg.HostAnomalyRateThreshold = HostAnomalyRateThreshold;
- Cfg.AnomalyDetectionGroupingMethod = web_client_api_request_v1_data_group(AnomalyDetectionGroupingMethod.c_str(), RRDR_GROUPING_AVERAGE);
- Cfg.AnomalyDetectionQueryDuration = AnomalyDetectionQueryDuration;
+ cfg->host_anomaly_rate_threshold = host_anomaly_rate_threshold;
+ cfg->anomaly_detection_grouping_method =
+ time_grouping_parse(anomaly_detection_grouping_method.c_str(), RRDR_GROUPING_AVERAGE);
+ cfg->anomaly_detection_query_duration = anomaly_detection_query_duration;
+ cfg->dimension_anomaly_score_threshold = dimension_anomaly_rate_threshold;
- 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->hosts_to_skip = config_get(config_section_ml, "hosts to skip from training", "!*");
+ cfg->sp_host_to_skip = simple_pattern_create(cfg->hosts_to_skip.c_str(), NULL, SIMPLE_PATTERN_EXACT, true);
// 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(Cfg.ChartsToSkip.c_str(), NULL, SIMPLE_PATTERN_EXACT);
+ cfg->charts_to_skip = "anomaly_detection.* ";
+ cfg->charts_to_skip += config_get(config_section_ml, "charts to skip from training", "netdata.*");
+ cfg->sp_charts_to_skip = simple_pattern_create(cfg->charts_to_skip.c_str(), NULL, SIMPLE_PATTERN_EXACT, true);
+
+ cfg->stream_anomaly_detection_charts = config_get_boolean(config_section_ml, "stream anomaly detection charts", true);
+
+ cfg->num_training_threads = num_training_threads;
+ cfg->flush_models_batch_size = flush_models_batch_size;
- Cfg.StreamADCharts = config_get_boolean(ConfigSectionML, "stream anomaly detection charts", true);
+ cfg->enable_statistics_charts = enable_statistics_charts;
}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-14 07:23:02 +0000