1 files changed, 139 insertions, 0 deletions
diff --git a/src/ml/Config.cc b/src/ml/Config.cc
new file mode 100644
index 000000000..c6a750995
--- /dev/null
+++ b/src/ml/Config.cc
@@ -0,0 +1,139 @@
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+#include "ml-private.h"
+
+/*
+ * Global configuration instance to be shared between training and
+ * prediction threads.
+ */
+ml_config_t 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 ml_config_load(ml_config_t *cfg) {
+    const char *config_section_ml = CONFIG_SECTION_ML;
+
+    bool enable_anomaly_detection = config_get_boolean(config_section_ml, "enabled", true);
+
+    /*
+     * Read values
+     */
+
+    unsigned max_train_samples = config_get_number(config_section_ml, "maximum num samples to train", 6 * 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", 3 * 3600);
+
+    unsigned num_models_to_use = config_get_number(config_section_ml, "number of models per dimension", 18);
+    unsigned delete_models_older_than = config_get_number(config_section_ml, "delete models older than", 60 * 60 * 24 * 7);
+
+    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);
+
+    double dimension_anomaly_rate_threshold = config_get_float(config_section_ml, "dimension anomaly score threshold", 0.99);
+
+    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);
+
+    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);
+
+    size_t suppression_window = config_get_number(config_section_ml, "dimension anomaly rate suppression window", 900);
+    size_t suppression_threshold = config_get_number(config_section_ml, "dimension anomaly rate suppression threshold", suppression_window / 2);
+
+    bool enable_statistics_charts = config_get_boolean(config_section_ml, "enable statistics charts", false);
+
+    /*
+     * Clamp
+     */
+
+    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);
+    delete_models_older_than = clamp<unsigned>(delete_models_older_than, 60 * 60 * 24 * 1, 60 * 60 * 24 * 7);
+
+    diff_n = clamp(diff_n, 0u, 1u);
+    smooth_n = clamp(smooth_n, 0u, 5u);
+    lag_n = clamp(lag_n, 1u, 5u);
+
+    random_sampling_ratio = clamp(random_sampling_ratio, 0.2, 1.0);
+    max_kmeans_iters = clamp(max_kmeans_iters, 500u, 1000u);
+
+    dimension_anomaly_rate_threshold = clamp(dimension_anomaly_rate_threshold, 0.01, 5.00);
+
+    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);
+
+    suppression_window = clamp<size_t>(suppression_window, 1, max_train_samples);
+    suppression_threshold = clamp<size_t>(suppression_threshold, 1, suppression_window);
+
+     /*
+     * Validate
+     */
+
+    if (min_train_samples >= max_train_samples) {
+        netdata_log_error("invalid min/max train samples found (%u >= %u)", min_train_samples, max_train_samples);
+
+        min_train_samples = 1 * 3600;
+        max_train_samples = 6 * 3600;
+    }
+
+    /*
+     * Assign to config instance
+     */
+
+    cfg->enable_anomaly_detection = enable_anomaly_detection;
+
+    cfg->max_train_samples = max_train_samples;
+    cfg->min_train_samples = min_train_samples;
+    cfg->train_every = train_every;
+
+    cfg->num_models_to_use = num_models_to_use;
+    cfg->delete_models_older_than = delete_models_older_than;
+
+    cfg->diff_n = diff_n;
+    cfg->smooth_n = smooth_n;
+    cfg->lag_n = lag_n;
+
+    cfg->random_sampling_ratio = random_sampling_ratio;
+    cfg->max_kmeans_iters = max_kmeans_iters;
+
+    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->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->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->suppression_window = suppression_window;
+    cfg->suppression_threshold = suppression_threshold;
+
+    cfg->enable_statistics_charts = enable_statistics_charts;
+}