Use anomaly IDs #42

analytics/pattern_detection_model.py

@@ -24,8 +24,8 @@ def segments_box(segments):
 
 class PatternDetectionModel:
 
-    def __init__(self, predictor_id, pattern):
+    def __init__(self, analytic_unit_id, pattern):
-        self.predictor_id = predictor_id
+        self.analytic_unit_id = analytic_unit_id
         self.pattern = pattern
 
         self.__load_anomaly_config()
@@ -101,16 +101,16 @@ class PatternDetectionModel:
             return StepDetector(pattern)
 
     def __load_anomaly_config(self):
-        with open(os.path.join(config.ANOMALIES_FOLDER, self.predictor_id + ".json"), 'r') as config_file:
+        with open(os.path.join(config.ANOMALIES_FOLDER, self.analytic_unit_id + ".json"), 'r') as config_file:
             self.anomaly_config = json.load(config_file)
 
     def __save_model(self):
-        logger.info("Save model '%s'" % self.predictor_id)
+        logger.info("Save model '%s'" % self.analytic_unit_id)
-        model_filename = os.path.join(config.MODELS_FOLDER, self.predictor_id + ".m")
+        model_filename = os.path.join(config.MODELS_FOLDER, self.analytic_unit_id + ".m")
         self.model.save(model_filename)
 
     def __load_model(self, pattern):
-        logger.info("Load model '%s'" % self.predictor_id)
+        logger.info("Load model '%s'" % self.analytic_unit_id)
         model_filename = os.path.join(config.MODELS_FOLDER, self.pattern + ".m")
         if os.path.exists(model_filename):
             self.model = self.__create_model(pattern)

analytics/server.py

@@ -28,7 +28,7 @@ def handleTask(text):
 
         socket.send_string(json.dumps({
             'task': task['type'],
-            'predictor_id': task['predictor_id'],
+            'analyticUnitId': task['analyticUnitId'],
             '_taskId': task['_taskId'],
             'status': "in progress"
         }))

analytics/worker.py

@@ -38,18 +38,19 @@ class Worker(object):
     def add_task(self, task):
         self.queue.put(task)
 
+    # TODO: get task as an object built from json
     def do_task(self, task):
         try:
             type = task['type']
-            predictor_id = task['predictor_id']
+            analytic_unit_id = task['analyticUnitId']
             if type == "predict":
-                last_prediction_time = task['last_prediction_time']
+                last_prediction_time = task['lastPredictionTime']
                 pattern = task['pattern']
-                result = self.do_predict(predictor_id, last_prediction_time, pattern)
+                result = self.do_predict(analytic_unit_id, last_prediction_time, pattern)
             elif type == "learn":
                 segments = task['segments']
                 pattern = task['pattern']
-                result = self.do_learn(predictor_id, segments, pattern)
+                result = self.do_learn(analytic_unit_id, segments, pattern)
             else:
                 result = {
                     'status': "failed",
@@ -59,51 +60,54 @@ class Worker(object):
             #traceback.extract_stack()
             error_text = traceback.format_exc()
             logger.error("Exception: '%s'" % error_text)
+            # TODO: move result to a class which renders to json for messaging to analytics
             result = {
                 'task': type,
                 'status': "failed",
-                'predictor_id': predictor_id,
+                'analyticUnitId': analytic_unit_id,
                 'error': str(e)
             }
         return result
 
-    def do_learn(self, predictor_id, segments, pattern):
+    def do_learn(self, analytic_unit_id, segments, pattern):
-        model = self.get_model(predictor_id, pattern)
+        model = self.get_model(analytic_unit_id, pattern)
         model.synchronize_data()
         last_prediction_time = model.learn(segments)
         # TODO: we should not do predict before labeling in all models, not just in drops
+        
         if pattern == 'drops' and len(segments) == 0:
+            # TODO: move result to a class which renders to json for messaging to analytics
             result = {
                 'status': 'success',
-                'predictor_id': predictor_id,
+                'analyticUnitId': analytic_unit_id,
                 'segments': [],
-                'last_prediction_time': last_prediction_time
+                'lastPredictionTime': last_prediction_time
             }
         else:
-            result = self.do_predict(predictor_id, last_prediction_time, pattern)
+            result = self.do_predict(analytic_unit_id, last_prediction_time, pattern)
             
         result['task'] = 'learn'
         return result
 
-    def do_predict(self, predictor_id, last_prediction_time, pattern):
+    def do_predict(self, analytic_unit_id, last_prediction_time, pattern):
-        model = self.get_model(predictor_id, pattern)
+        model = self.get_model(analytic_unit_id, pattern)
         model.synchronize_data()
         segments, last_prediction_time = model.predict(last_prediction_time)
         return {
             'task': "predict",
             'status': "success",
-            'analyticUnitId': predictor_id,
+            'analyticUnitId': analytic_unit_id,
             'segments': segments,
             'lastPredictionTime': last_prediction_time
         }
 
-    def get_model(self, predictor_id, pattern):
+    def get_model(self, analytic_unit_id, pattern):
-        if predictor_id not in self.models_cache:
+        if analytic_unit_id not in self.models_cache:
             if pattern.find('general') != -1:
-                model = AnomalyModel(predictor_id)
+                model = AnomalyModel(analytic_unit_id)
             else:
-                model = PatternDetectionModel(predictor_id, pattern)
+                model = PatternDetectionModel(analytic_unit_id, pattern)
-            self.models_cache[predictor_id] = model
+            self.models_cache[analytic_unit_id] = model
-        return self.models_cache[predictor_id]
+        return self.models_cache[analytic_unit_id]
 
 

server/src/controllers/analytics_controller.ts

@@ -64,7 +64,7 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
   let pattern = unit.type;
   let task = {
     type: 'predict',
-    predictor_id: id,
+    analyticUnitId: id,
     pattern,
     lastPredictionTime: unit.lastPredictionTime
   };