One panel - one worker #62

analytics/Codestyle.md

@@ -1,6 +1,10 @@
+# Type hints
+
+Please use: https://www.python.org/dev/peps/pep-0484/
+
 # Line endings
 
-We use CRLS everywhere
+We use LF everywhere
 
 # Imports
 

analytics/analytic_unit_manager.py

@@ -0,0 +1,50 @@
+from typing import Dict
+
+import detectors
+from analytic_unit_worker import AnalyticUnitWorker
+
+analytic_unit_id = str
+analytic_workers: Dict[analytic_unit_id, AnalyticUnitWorker] = Dict()
+
+def get_detector(self, analytic_unit_type) -> detectors.Detector:
+    if analytic_unit_type == 'GENERAL':
+        detector = detectors.GeneralDetector()
+    else:
+        detector = detectors.PatternDetector(analytic_unit_type)
+    return detector
+
+def ensure_worker(analytic_unit_id, analytic_unit_type) -> AnalyticUnitWorker:
+    if analytic_unit_id in analytic_workers:
+        # TODO: check that type is the same
+        return analytic_workers[analytic_unit_id]
+    detector = get_detector(analytic_unit_type)
+    worker = AnalyticUnitWorker(analytic_unit_id, detector)
+    analytic_workers[analytic_unit_id] = worker
+    return worker
+
+async def handle_analytic_task(task):
+    try:
+        worker = ensure_worker(task['analyticUnitId'], task['type'])
+        payload = task['payload']
+        if type == "PREDICT":
+            result_payload = await worker.do_predict(analytic_unit_id, payload)
+        elif type == "LEARN":
+            result_payload = await worker.do_learn(analytic_unit_id, payload)
+        else:
+            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 = {
+            'status': "FAILED",
+            'error': str(e)
+        }
+    return {
+        'status': 'SUCCESS',
+        'payload': result_payload
+    }
+
+

analytics/analytic_unit_worker.py

@@ -7,57 +7,20 @@ import traceback
 import time
 
 
-
 logger = logging.getLogger('AnalyticUnitWorker')
 
 
 class AnalyticUnitWorker:
 
-    def get_detector(self, analytic_unit_id, pattern_type):
+    def __init__(self, analytic_unit_id: str, detector: detectors.Detector):
-        if analytic_unit_id not in self.detectors_cache:
+        self.analytic_unit_id = analytic_unit_id
-            if pattern_type == 'GENERAL':
+        self.detector = detector
-                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_payload = await self.do_predict(analytic_unit_id, payload)
-            elif type == "LEARN":
-                result_payload = await self.do_learn(analytic_unit_id, payload)
-            else:
-                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 = {
-                'status': "FAILED",
-                'error': str(e)
-            }
-        return {
-            'status': 'SUCCESS',
-            'payload': result_payload
-        }
 
     async def do_learn(self, analytic_unit_id, payload) -> None:
         pattern = payload['pattern']
         segments = payload['segments']
         data = payload['data'] # [time, value][]
-
+        await self.detector.train(data, segments)
-        detector = self.get_detector(analytic_unit_id, pattern)
-        await detector.learn(segments)
 
     async def do_predict(self, analytic_unit_id, payload):
         pattern = payload['pattern']

analytics/detectors/detector.py

@@ -5,9 +5,9 @@ from pandas import DataFrame
 class Detector(ABC):
 
     @abstractmethod
-    def fit(self, dataframe: DataFrame, segments: list):
+    async def train(self, dataframe: DataFrame, segments: list):
         pass
 
     @abstractmethod
-    def predict(self, dataframe: DataFrame) -> list:
+    async def predict(self, dataframe: DataFrame) -> list:
         pass

analytics/detectors/general_detector/general_detector.py

@@ -1,4 +1,5 @@
 from detectors.general_detector.supervised_algorithm import SupervisedAlgorithm
+from detectors import Detector
 import utils
 # from grafana_data_provider import GrafanaDataProvider
 from data_preprocessor import data_preprocessor
@@ -15,15 +16,13 @@ NANOSECONDS_IN_MS = 1000000
 logger = logging.getLogger('analytic_toolset')
 
 
-class GeneralDetector:
+class GeneralDetector(Detector):
 
-    def __init__(self, anomaly_name):
+    def __init__(self):
-        self.anomaly_name = anomaly_name
         self.model = None
         self.__load_model()
 
-    async def learn(self, segments, data):
+    async def train(self, segments, data):
-        logger.info("Start to learn for anomaly_name='%s'" % self.anomaly_name)
 
         confidence = 0.02
         dataframe = data # make dataframae from array
@@ -40,7 +39,7 @@ class GeneralDetector:
             segments
         )
 
-        self.model = self.create_algorithm()
+        self.model = SupervisedAlgorithm()
         await self.model.fit(train_augmented, confidence)
         if len(segments) > 0:
             last_dataframe_time = dataframe.iloc[-1]['timestamp']
@@ -52,7 +51,7 @@ class GeneralDetector:
         logger.info("Learning is finished for anomaly_name='%s'" % self.anomaly_name)
         return last_prediction_time
 
-    async def predict(self, last_prediction_time):
+    async def predict(self, data):
         logger.info("Start to predict for anomaly type='%s'" % self.anomaly_name)
         last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms')
 
@@ -83,15 +82,3 @@ class GeneralDetector:
 
         logger.info("Predicting is finished for anomaly type='%s'" % self.anomaly_name)
         return predicted_anomalies, last_prediction_time
-
-
-    def create_algorithm(self):
-        return SupervisedAlgorithm()
-
-    def __save_model(self):
-        pass
-        # TODO: use data_service to save anything
-
-    def __load_model(self):
-        pass
-        # TODO: use data_service to save anything

analytics/detectors/pattern_detector.py

@@ -11,9 +11,10 @@ import config
 
 import pandas as pd
 
+from detectors import Detector
 
-logger = logging.getLogger('analytic_toolset')
 
+logger = logging.getLogger('analytic_toolset')
 
 
 def resolve_model_by_pattern(pattern: str) -> models.Model:
@@ -28,30 +29,20 @@ def resolve_model_by_pattern(pattern: str) -> models.Model:
     raise ValueError('Unknown pattern "%s"' % pattern)
 
 
-class PatternDetector:
+class PatternDetector(Detector):
 
-    def __init__(self, analytic_unit_id, pattern_type):
+    def __init__(self, pattern_type):
-        self.analytic_unit_id = analytic_unit_id
         self.pattern_type = pattern_type
-
-        self.model = None
-        self.__load_model(pattern_type)
-
-    async def learn(self, segments, data):
         self.model = resolve_model_by_pattern(self.pattern_type)
-        window_size = 200
+        window_size = 100
 
+    async def train(self, dataframe: pd.DataFrame, segments: list):
         # TODO: pass only part of dataframe that has segments
-        self.model.fit(dataframe, segments, data)
+        self.model.fit(dataframe, data, segments)
         self.__save_model()
         return 0
 
-    async def predict(self, last_prediction_time, data):
+    async def predict(self, data):
-        if self.model is None:
-            return [], last_prediction_time
-
-        window_size = 100
-        last_prediction_time = pd.to_datetime(last_prediction_time, unit='ms')
 
         start_index = self.data_prov.get_upper_bound(last_prediction_time)
         start_index = max(0, start_index - window_size)
@@ -73,12 +64,3 @@ class PatternDetector:
         last_dataframe_time = dataframe.iloc[-1]['timestamp']
         last_prediction_time = int(last_dataframe_time.timestamp() * 1000)
         return segments, last_prediction_time
-        # return predicted_anomalies, last_prediction_time
-
-    def __save_model(self):
-        pass
-        # TODO: use data_service to save anything
-
-    def __load_model(self, pattern):
-        pass
-        # TODO: use data_service to save anything

analytics/server.py

@@ -5,13 +5,13 @@ import sys
 import asyncio
 
 import services
-from analytic_unit_worker import AnalyticUnitWorker
+from analytic_unit_manager import handle_analytic_task
 
 
 root = logging.getLogger()
 logger = logging.getLogger('SERVER')
 
-worker: AnalyticUnitWorker = None
+
 server_service: services.ServerService = None
 data_service: services.DataService = None
 
@@ -28,6 +28,8 @@ logging_handler.setFormatter(logging_formatter)
 root.addHandler(logging_handler)
 
 
+
+
 async def handle_task(task: object):
     try:
 
@@ -43,7 +45,7 @@ async def handle_task(task: object):
         message = services.server_service.ServerMessage('TASK_RESULT', task_result_payload)
         await server_service.send_message(message)
 
-        res = await worker.do_task(task)
+        res = await handle_analytic_task(task)
         res['_id'] = task['_id']
 
         message = services.server_service.ServerMessage('TASK_RESULT', res)