threshold begin

server/Cargo.lock

@@ -482,6 +482,7 @@ name = "hastic"
 version = "0.0.1"
 dependencies = [
  "anyhow",
+ "async-trait",
  "bincode",
  "chrono",
  "config",

server/Cargo.toml

@@ -27,6 +27,7 @@ linfa = "0.5.0"
 linfa-svm = { version="0.5.0", features=["serde"] }
 ndarray = "0.15.3"
 bincode = "1.3.3"
+async-trait = "0.1.51"
 
 # TODO: remove this from prod
 # plotlib = "0.5.1"

server/src/services/analytic_service/analytic_service.rs

@@ -1,8 +1,8 @@
-use super::analytic_unit::types::{AnalyticUnitConfig, PatternDetectorConfig};
+use super::analytic_unit::types::{AnalyticUnitConfig, PatternConfig};
 use super::types::{self, DetectionRunnerConfig, LearningTrain};
 use super::{
     analytic_client::AnalyticClient,
-    analytic_unit::pattern_detector::{self, LearningResults, PatternDetector},
+    analytic_unit::pattern_analytic_unit::{self, LearningResults, PatternAnalyticUnit},
     types::{AnalyticServiceMessage, DetectionTask, LearningStatus, RequestType, ResponseType},
 };
 
@@ -51,9 +51,11 @@ async fn segment_to_segdata(ms: &MetricService, segment: &Segment) -> anyhow::Re
 pub struct AnalyticService {
     metric_service: MetricService,
     segments_service: SegmentsService,
-    learning_results: Option<LearningResults>,
+
+    analytic_unit_learning_results: Option<LearningResults>,
     analytic_unit_config: AnalyticUnitConfig,
-    learning_status: LearningStatus,
+    analytic_unit_learning_status: LearningStatus,
+
     tx: mpsc::Sender<AnalyticServiceMessage>,
     rx: mpsc::Receiver<AnalyticServiceMessage>,
 
@@ -82,13 +84,13 @@ impl AnalyticService {
             segments_service,
 
             // TODO: get it from persistance
-            learning_results: None,
-            analytic_unit_config: AnalyticUnitConfig::PatternDetector(PatternDetectorConfig {
+            analytic_unit_learning_results: None,
+            analytic_unit_config: AnalyticUnitConfig::Pattern(PatternConfig {
                 correlation_score: 0.95,
                 model_score: 0.95
             }),
 
-            learning_status: LearningStatus::Initialization,
+            analytic_unit_learning_status: LearningStatus::Initialization,
             tx,
             rx,
 
@@ -111,10 +113,10 @@ impl AnalyticService {
     fn run_detection_task(&self, task: DetectionTask) {
         // TODO: save handler of the task
         tokio::spawn({
-            let lr = self.learning_results.as_ref().unwrap().clone();
+            let lr = self.analytic_unit_learning_results.as_ref().unwrap().clone();
             let ms = self.metric_service.clone();
             async move {
-                AnalyticService::get_pattern_detection(task.sender, lr, ms, task.from, task.to)
+                AnalyticService::get_detections(task.sender, lr, ms, task.from, task.to)
                     .await;
             }
         });
@@ -126,7 +128,7 @@ impl AnalyticService {
         }
         // TODO: save handler of the task
         self.runner_handler = Some(tokio::spawn({
-            let lr = self.learning_results.as_ref().unwrap().clone();
+            let lr = self.analytic_unit_learning_results.as_ref().unwrap().clone();
             let ms = self.metric_service.clone();
             async move {
                 // TODO: implement
@@ -142,7 +144,7 @@ impl AnalyticService {
                     self.learning_handler = None;
                 }
                 self.learning_handler = Some(tokio::spawn({
-                    self.learning_status = LearningStatus::Starting;
+                    self.analytic_unit_learning_status = LearningStatus::Starting;
                     let tx = self.tx.clone();
                     let ms = self.metric_service.clone();
                     let ss = self.segments_service.clone();
@@ -152,7 +154,7 @@ impl AnalyticService {
                 }));
             }
             RequestType::RunDetection(task) => {
-                if self.learning_status == LearningStatus::Initialization {
+                if self.analytic_unit_learning_status == LearningStatus::Initialization {
                     match task
                         .sender
                         .send(Err(anyhow::format_err!("Analytics in initialization")))
@@ -165,21 +167,21 @@ impl AnalyticService {
                     }
                     return;
                 }
-                if self.learning_status == LearningStatus::Ready {
+                if self.analytic_unit_learning_status == LearningStatus::Ready {
                     self.run_detection_task(task);
                 } else {
                     self.learning_waiters.push(task);
                 }
             }
             RequestType::GetStatus(tx) => {
-                tx.send(self.learning_status.clone()).unwrap();
+                tx.send(self.analytic_unit_learning_status.clone()).unwrap();
             }
             RequestType::GetLearningTrain(tx) => {
-                if self.learning_results.is_none() {
+                if self.analytic_unit_learning_results.is_none() {
                     tx.send(LearningTrain::default()).unwrap();
                 } else {
                     tx.send(
-                        self.learning_results
+                        self.analytic_unit_learning_results
                             .as_ref()
                             .unwrap()
                             .learning_train
@@ -197,11 +199,11 @@ impl AnalyticService {
     fn consume_response(&mut self, res: types::ResponseType) {
         match res {
             // TODO: handle when learning panic
-            ResponseType::LearningStarted => self.learning_status = LearningStatus::Learning,
+            ResponseType::LearningStarted => self.analytic_unit_learning_status = LearningStatus::Learning,
             ResponseType::LearningFinished(results) => {
                 self.learning_handler = None;
-                self.learning_results = Some(results);
-                self.learning_status = LearningStatus::Ready;
+                self.analytic_unit_learning_results = Some(results);
+                self.analytic_unit_learning_status = LearningStatus::Ready;
 
                 // TODO: run tasks from self.learning_waiter
                 while self.learning_waiters.len() > 0 {
@@ -219,13 +221,13 @@ impl AnalyticService {
             }
             ResponseType::LearningFinishedEmpty => {
                 // TODO: drop all learning_waiters with empty results
-                self.learning_results = None;
-                self.learning_status = LearningStatus::Initialization;
+                self.analytic_unit_learning_results = None;
+                self.analytic_unit_learning_status = LearningStatus::Initialization;
             }
             ResponseType::LearningDatasourceError => {
                 // TODO: drop all learning_waiters with error
-                self.learning_results = None;
-                self.learning_status = LearningStatus::Error;
+                self.analytic_unit_learning_results = None;
+                self.analytic_unit_learning_status = LearningStatus::Error;
             }
         }
     }
@@ -311,7 +313,7 @@ impl AnalyticService {
             }
         }
 
-        let lr = PatternDetector::learn(&learn_tss, &learn_anti_tss).await;
+        let lr = PatternAnalyticUnit::learn(&learn_tss, &learn_anti_tss).await;
         match tx
             .send(AnalyticServiceMessage::Response(
                 ResponseType::LearningFinished(lr),
@@ -323,14 +325,14 @@ impl AnalyticService {
         }
     }
 
-    async fn get_pattern_detection(
+    async fn get_detections(
         tx: oneshot::Sender<anyhow::Result<Vec<Segment>>>,
         lr: LearningResults,
         ms: MetricService,
         from: u64,
         to: u64,
     ) {
-        let pt = pattern_detector::PatternDetector::new(lr);
+        let pt = pattern_analytic_unit::PatternAnalyticUnit::new(lr);
         let mr = ms.query(from, to, DETECTION_STEP).await.unwrap();
 
         if mr.data.keys().len() == 0 {
@@ -367,58 +369,5 @@ impl AnalyticService {
         return;
     }
 
-    // TODO: move this to another analytic unit
-    async fn get_threshold_detections(
-        &self,
-        from: u64,
-        to: u64,
-        step: u64,
-        threashold: f64,
-    ) -> anyhow::Result<Vec<Segment>> {
-        let mr = self.metric_service.query(from, to, step).await?;
 
-        if mr.data.keys().len() == 0 {
-            return Ok(Vec::new());
-        }
-
-        let key = mr.data.keys().nth(0).unwrap();
-        let ts = &mr.data[key];
-
-        let mut result = Vec::<Segment>::new();
-        let mut from: Option<u64> = None;
-        for (t, v) in ts {
-            if *v > threashold {
-                if from.is_some() {
-                    continue;
-                } else {
-                    from = Some(*t);
-                }
-            } else {
-                if from.is_some() {
-                    result.push(Segment {
-                        // TODO: persist detections together with id
-                        id: Some(get_random_str(ID_LENGTH)),
-                        from: from.unwrap(),
-                        to: *t,
-                        segment_type: SegmentType::Detection,
-                    });
-                    from = None;
-                }
-            }
-        }
-
-        // TODO: don't repeat myself
-        if from.is_some() {
-            result.push(Segment {
-                id: Some(get_random_str(ID_LENGTH)),
-                from: from.unwrap(),
-                to,
-                segment_type: SegmentType::Detection,
-            });
-        }
-
-        // TODO: decide what to do it from is Some() in the end
-
-        Ok(result)
-    }
 }

server/src/services/analytic_service/analytic_unit/mod.rs

@@ -1,6 +1,12 @@
-pub mod pattern_detector;
+pub mod pattern_analytic_unit;
+pub mod threshold_analytic_unit;
 pub mod types;
 
+use async_trait::async_trait;
+
+#[async_trait]
 trait AnalyticUnit<C> {
-    
-}
+    async fn learn(reads: &Vec<Vec<(u64, f64)>>, anti_reads: &Vec<Vec<(u64, f64)>>);
+    fn detect(&self, ts: &Vec<(u64, f64)>) -> Vec<(u64, u64)>;
+}
+

server/src/services/analytic_service/analytic_unit/pattern_analytic_unit.rs

@@ -55,7 +55,7 @@ pub type Features = [f64; FEATURES_SIZE];
 pub const SCORE_THRESHOLD: f64 = 0.95;
 
 #[derive(Clone)]
-pub struct PatternDetector {
+pub struct PatternAnalyticUnit {
     learning_results: LearningResults,
 }
 
@@ -67,9 +67,9 @@ fn nan_to_zero(n: f64) -> f64 {
 }
 
 // TODO: move this to loginc of analytic unit
-impl PatternDetector {
-    pub fn new(learning_results: LearningResults) -> PatternDetector {
-        PatternDetector { learning_results }
+impl PatternAnalyticUnit {
+    pub fn new(learning_results: LearningResults) -> PatternAnalyticUnit {
+        PatternAnalyticUnit { learning_results }
     }
 
     pub async fn learn(
@@ -86,7 +86,7 @@ impl PatternDetector {
 
         for r in reads {
             let xs: Vec<f64> = r.iter().map(|e| e.1).map(nan_to_zero).collect();
-            let fs = PatternDetector::get_features(&xs);
+            let fs = PatternAnalyticUnit::get_features(&xs);
 
             records_raw.push(fs);
             targets_raw.push(true);
@@ -95,7 +95,7 @@ impl PatternDetector {
 
         for r in anti_reads {
             let xs: Vec<f64> = r.iter().map(|e| e.1).map(nan_to_zero).collect();
-            let fs = PatternDetector::get_features(&xs);
+            let fs = PatternAnalyticUnit::get_features(&xs);
             records_raw.push(fs);
             targets_raw.push(false);
             anti_patterns.push(xs);
@@ -168,7 +168,7 @@ impl PatternDetector {
                     for j in 0..p.len() {
                         backet.push(nan_to_zero(ts[i + j].1));
                     }
-                    let score = PatternDetector::corr_aligned(p, &backet);
+                    let score = PatternAnalyticUnit::corr_aligned(p, &backet);
                     if score > pattern_match_score {
                         pattern_match_score = score;
                         pattern_match_len = p.len();
@@ -182,7 +182,7 @@ impl PatternDetector {
                     for j in 0..p.len() {
                         backet.push(nan_to_zero(ts[i + j].1));
                     }
-                    let score = PatternDetector::corr_aligned(p, &backet);
+                    let score = PatternAnalyticUnit::corr_aligned(p, &backet);
                     if score > anti_pattern_match_score {
                         anti_pattern_match_score = score;
                     }
@@ -194,7 +194,7 @@ impl PatternDetector {
                 for j in 0..pattern_match_len {
                     backet.push(nan_to_zero(ts[i + j].1));
                 }
-                let fs = PatternDetector::get_features(&backet);
+                let fs = PatternAnalyticUnit::get_features(&backet);
                 let detected = self
                     .learning_results
                     .model
@@ -245,8 +245,6 @@ impl PatternDetector {
             return 0.;
         }
 
-        // TODO: case when denominator = 0
-
         let result: f64 = numerator / denominator;
 
         // assert!(result.abs() <= 1.01);

server/src/services/analytic_service/analytic_unit/threshold_analytic_unit.rs

@@ -0,0 +1,40 @@
+use super::types::ThresholdConfig;
+
+struct ThresholdDetector {
+    config: ThresholdConfig
+}
+
+impl ThresholdDetector {
+    fn new(config: ThresholdConfig) -> ThresholdDetector {
+        ThresholdDetector{ config }
+    }
+
+    pub fn detect(&self, ts: &Vec<(u64, f64)>) -> Vec<(u64, u64)> {
+
+        let mut result = Vec::<(u64, u64)>::new();
+        let mut from: Option<u64> = None;
+        for (t, v) in ts {
+            if *v > self.config.threashold {
+                if from.is_some() {
+                    continue;
+                } else {
+                    from = Some(*t);
+                }
+            } else {
+                if from.is_some() {
+                    result.push((from.unwrap(), *t));
+                    from = None;
+                }
+            }
+        }
+
+        // TODO: don't repeat myself
+        if from.is_some() {
+            result.push((from.unwrap(), ts.last().unwrap().0));
+        }
+
+        // TODO: decide what to do it from is Some() in the end
+
+        result
+    }
+}

server/src/services/analytic_service/analytic_unit/types.rs

@@ -1,12 +1,18 @@
 use serde::{Deserialize, Serialize};
 
 #[derive(Debug, Serialize, Deserialize, Clone)]
-pub struct PatternDetectorConfig {
+pub struct PatternConfig {
     pub correlation_score: f32,
     pub model_score: f32
 }
 
+#[derive(Debug, Serialize, Deserialize, Clone)]
+pub struct ThresholdConfig {
+    pub threashold: f64,
+}
+
 #[derive(Debug, Serialize, Deserialize, Clone)]
 pub enum AnalyticUnitConfig {
-    PatternDetector(PatternDetectorConfig)
+    Pattern(PatternConfig),
+    Threshold(ThresholdConfig)
 }

server/src/services/analytic_service/types.rs

@@ -1,6 +1,6 @@
 use crate::services::segments_service::Segment;
 
-use super::analytic_unit::{pattern_detector::{self, LearningResults}, types::AnalyticUnitConfig};
+use super::analytic_unit::{pattern_analytic_unit::{self, LearningResults}, types::AnalyticUnitConfig};
 
 use anyhow::Result;
 use serde::Serialize;
@@ -18,7 +18,7 @@ pub enum LearningStatus {
 // TODO: move to analytic_unit config of pattern detector
 #[derive(Clone, Serialize, Debug)]
 pub struct LearningTrain {
-    pub features: Vec<pattern_detector::Features>,
+    pub features: Vec<pattern_analytic_unit::Features>,
     pub target: Vec<bool>,
 }