Cancel learning on analytic unit deletion #266 (#269)

* basic cancelation in analytics

* cancelation task on node

* basic cancelation in analytics

analytics/analytics/analytic_unit_manager.py

@@ -39,7 +39,7 @@ class AnalyticUnitManager:
         self.analytic_workers: Dict[AnalyticUnitId, AnalyticUnitWorker] = dict()
         self.workers_executor = ThreadPoolExecutor(max_workers=WORKERS_EXECUTORS)
 
-    def __ensure_worker(self, analytic_unit_id, analytic_unit_type) -> AnalyticUnitWorker:
+    def __ensure_worker(self, analytic_unit_id: AnalyticUnitId, analytic_unit_type) -> AnalyticUnitWorker:
         if analytic_unit_id in self.analytic_workers:
             # TODO: check that type is the same
             return self.analytic_workers[analytic_unit_id]
@@ -50,12 +50,21 @@ class AnalyticUnitManager:
 
     async def handle_analytic_task(self, task):
         try:
+            analytic_unit_id: AnalyticUnitId = task['analyticUnitId']
+
+            if task['type'] == 'CANCEL':
+                if analytic_unit_id in self.analytic_workers:
+                    self.analytic_workers[analytic_unit_id].cancel()
+                return {
+                    'status': 'SUCCESS'
+                }
+
             payload = task['payload']
-            worker = self.__ensure_worker(task['analyticUnitId'], payload['pattern'])
+            worker = self.__ensure_worker(analytic_unit_id, payload['pattern'])
             data = prepare_data(payload['data'])
             result_payload = {}
             if task['type'] == 'LEARN':
-                result_payload = await worker.do_learn(payload['segments'], data, payload['cache'])
+                result_payload = await worker.do_train(payload['segments'], data, payload['cache'])
             elif task['type'] == 'PREDICT':
                 result_payload = await worker.do_predict(data, payload['cache'])
             else:

analytics/analytics/analytic_unit_worker.py

@@ -4,7 +4,7 @@ import logging
 import pandas as pd
 from typing import Optional
 from models import AnalyticUnitCache
-from concurrent.futures import Executor
+from concurrent.futures import Executor, CancelledError
 import asyncio
 
 logger = logging.getLogger('AnalyticUnitWorker')
@@ -14,16 +14,26 @@ class AnalyticUnitWorker:
 
     def __init__(self, analytic_unit_id: str, detector: detectors.Detector, executor: Executor):
         self.analytic_unit_id = analytic_unit_id
-        self.detector = detector
+        self._detector = detector
-        self.executor: Executor = executor
+        self._executor: Executor = executor
+        self._training_feature: asyncio.Future = None
 
-    async def do_learn(
+    async def do_train(
         self, segments: list, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]
     ) -> AnalyticUnitCache:
-        new_cache: AnalyticUnitCache = await asyncio.get_event_loop().run_in_executor(
+        self._training_feature = asyncio.get_event_loop().run_in_executor(
-            self.executor, self.detector.train, data, segments, cache
+            self._executor, self._detector.train, data, segments, cache
         )
+        try:
+            new_cache: AnalyticUnitCache = await self._training_feature
             return new_cache
+        except CancelledError as e:
+            return cache
+        
 
     async def do_predict(self, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
-        return self.detector.predict(data, cache)
+        return self._detector.predict(data, cache)
+    
+    def cancel(self):
+        if self._training_feature is not None:
+            self._training_feature.cancel()

server/src/controllers/analytics_controller.ts

@@ -210,6 +210,12 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
   }
 }
 
+export async function remove(id: AnalyticUnit.AnalyticUnitId) {
+  let task = new AnalyticsTask(id, AnalyticsTaskType.CANCEL);
+  await runTask(task);
+  await AnalyticUnit.remove(id);
+}
+
 export async function deleteNonpredictedSegments(id, payload) {
   let lastPredictedSegments = await Segment.findMany(id, { labeled: false, deleted: false });
   let segmentsToRemove: Segment.Segment[];

server/src/models/analytics_task_model.ts

@@ -8,7 +8,8 @@ const UID_LENGTH = 16;
 export type AnalyticsTaskId = string;
 export enum AnalyticsTaskType { 
   LEARN = 'LEARN',
-  PREDICT = 'PREDICT'
+  PREDICT = 'PREDICT',
+  CANCEL = 'CANCEL'
 };
 
 export class AnalyticsTask {

server/src/routes/analytic_units_router.ts

@@ -1,3 +1,4 @@
+import * as AnalyticsController from '../controllers/analytics_controller';
 import * as AnalyticUnit from '../models/analytic_unit_model';
 
 import { createAnalyticUnitFromObject } from '../controllers/analytics_controller';
@@ -82,7 +83,7 @@ async function deleteUnit(ctx: Router.IRouterContext) {
     if(analyticUnitId === undefined) {
       throw new Error('Cannot delete undefined id');
     }
-    await AnalyticUnit.remove(analyticUnitId);
+    await AnalyticsController.remove(analyticUnitId);
     ctx.response.body = {
       code: 200,
       message: 'Success'