analytics: detector class + more types + remove Model.(save/load)

analytics/analytic_unit_worker.py

@@ -7,84 +7,65 @@ import traceback
 import time
 
 
-logger = logging.getLogger('WORKER')
 
+logger = logging.getLogger('AnalyticUnitWorker')
 
-class AnalyticUnitWorker(object):
-    detectors_cache = {}
 
-    # TODO: get task as an object built from json
+class AnalyticUnitWorker:
+
+    def get_detector(self, analytic_unit_id, pattern_type):
+        if analytic_unit_id not in self.detectors_cache:
+            if pattern_type == 'GENERAL':
+                detector = detectors.GeneralDetector(analytic_unit_id)
+            else:
+                detector = detectors.PatternDetector(analytic_unit_id, pattern_type)
+            self.detectors_cache[analytic_unit_id] = detector
+        return self.detectors_cache[analytic_unit_id]
+
+    def __init__(self, detector: detectors.Detector):
+        pass
+
     async def do_task(self, task):
         try:
             type = task['type']
             analytic_unit_id = task['analyticUnitId']
             payload = task['payload']
             if type == "PREDICT":
-                result = await self.do_predict(analytic_unit_id, payload)
+                result_payload = await self.do_predict(analytic_unit_id, payload)
             elif type == "LEARN":
-                result = await self.do_learn(analytic_unit_id, payload)
+                result_payload = await self.do_learn(analytic_unit_id, payload)
             else:
-                result = {
-                    'status': "FAILED",
-                    'error': "unknown type " + str(type)
-                }
+                raise ValueError('Unknown task type %s' % type) 
+
         except Exception as e:
             #traceback.extract_stack()
             error_text = traceback.format_exc()
             logger.error("do_task Exception: '%s'" % error_text)
             # TODO: move result to a class which renders to json for messaging to analytics
             result = {
-                'task': type,
                 'status': "FAILED",
-                'analyticUnitId': analytic_unit_id,
                 'error': str(e)
             }
-        return result
+        return {
+            'status': 'SUCCESS',
+            'payload': result_payload
+        }
 
-    async def do_learn(self, analytic_unit_id, payload):
+    async def do_learn(self, analytic_unit_id, payload) -> None:
         pattern = payload['pattern']
         segments = payload['segments']
         data = payload['data'] # [time, value][]
 
         detector = self.get_detector(analytic_unit_id, pattern)
-        detector.synchronize_data()
-        last_prediction_time = await detector.learn(segments)
-        # TODO: we should not do predict before labeling in all models, not just in drops
-
-        if pattern == 'DROP' and len(segments) == 0:
-            # TODO: move result to a class which renders to json for messaging to analytics
-            result = {
-                'status': 'SUCCESS',
-                'analyticUnitId': analytic_unit_id,
-                'segments': [],
-                'lastPredictionTime': last_prediction_time
-            }
-        else:
-            result = await self.do_predict(analytic_unit_id, last_prediction_time, pattern)
-
-        result['task'] = 'LEARN'
-        return result
+        await detector.learn(segments)
 
     async def do_predict(self, analytic_unit_id, payload):
         pattern = payload['pattern']
-        last_prediction_time = payload['lastPredictionTime']
+        data = payload['data'] # [time, value][]
 
         detector = self.get_detector(analytic_unit_id, pattern)
-        detector.synchronize_data()
-        segments, last_prediction_time = await detector.predict(last_prediction_time)
+        segments, last_prediction_time = await detector.predict(data)
         return {
-            'task': 'PREDICT',
-            'status': 'SUCCESS',
-            'analyticUnitId': analytic_unit_id,
             'segments': segments,
             'lastPredictionTime': last_prediction_time
         }
-
-    def get_detector(self, analytic_unit_id, pattern_type):
-        if analytic_unit_id not in self.detectors_cache:
-            if pattern_type == 'GENERAL':
-                detector = detectors.GeneralDetector(analytic_unit_id)
-            else:
-                detector = detectors.PatternDetector(analytic_unit_id, pattern_type)
-            self.detectors_cache[analytic_unit_id] = detector
-        return self.detectors_cache[analytic_unit_id]

analytics/detectors/__init__.py

@@ -1,3 +1,3 @@
+from detectors.detector import Detector
 from detectors.pattern_detector import PatternDetector
-# TODO: do something with general detector
 from detectors.general_detector import GeneralDetector

analytics/detectors/detector.py

@@ -0,0 +1,13 @@
+from abc import ABC, abstractmethod
+from pandas import DataFrame
+
+
+class Detector(ABC):
+
+    @abstractmethod
+    def fit(self, dataframe: DataFrame, segments: list):
+        pass
+
+    @abstractmethod
+    def predict(self, dataframe: DataFrame) -> list:
+        pass

analytics/models/model.py

@@ -1,17 +1,8 @@
 from abc import ABC, abstractmethod
 from pandas import DataFrame
-import pickle
 
-class Model(ABC):
 
-    def __init__(self):
-        """
-        Variables which are obtained as a result of fit() method 
-        should be stored in self.state dict
-        in order to be saved in model file
-        """
-        self.state = {}
-        self.segments = []
+class Model(ABC):
 
     @abstractmethod
     def fit(self, dataframe: DataFrame, segments: list):
@@ -20,11 +11,3 @@ class Model(ABC):
     @abstractmethod
     def predict(self, dataframe: DataFrame) -> list:
         pass
-
-    def save(self, model_filename: str):
-        with open(model_filename, 'wb') as file:
-            pickle.dump(self.state, file)
-
-    def load(self, model_filename: str):
-        with open(model_filename, 'rb') as f:
-            self.state = pickle.load(f)