Rename predict to detect #279 (#284)

* dummy rename

* fixes

* renaming in analytics

analytics/analytics/analytic_unit_manager.py

@@ -65,8 +65,8 @@ class AnalyticUnitManager:
         data = prepare_data(payload['data'])
         if task['type'] == 'LEARN':
             return await worker.do_train(payload['segments'], data, payload['cache'])
-        elif task['type'] == 'PREDICT':
+        elif task['type'] == 'DETECT':
-            return await worker.do_predict(data, payload['cache'])
+            return await worker.do_detect(data, payload['cache'])
 
         raise ValueError('Unknown task type "%s"' % task['type'])
 

analytics/analytics/analytic_unit_worker.py

@@ -30,8 +30,8 @@ class AnalyticUnitWorker:
         except CancelledError as e:
             return cache
 
-    async def do_predict(self, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
+    async def do_detect(self, data: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
-        return self._detector.predict(data, cache)
+        return self._detector.detect(data, cache)
 
     def cancel(self):
         if self._training_feature is not None:

analytics/analytics/detectors/detector.py

@@ -14,5 +14,5 @@ class Detector(ABC):
         pass
 
     @abstractmethod
-    def predict(self, dataframe: DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
+    def detect(self, dataframe: DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         pass

analytics/analytics/detectors/pattern_detector.py

@@ -42,17 +42,17 @@ class PatternDetector(Detector):
             'cache': new_cache
         }
 
-    def predict(self, dataframe: pd.DataFrame, cache: Optional[models.AnalyticUnitCache]) -> dict:
+    def detect(self, dataframe: pd.DataFrame, cache: Optional[models.AnalyticUnitCache]) -> dict:
         # TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
-        predicted = self.model.predict(dataframe, cache)
+        detected = self.model.detect(dataframe, cache)
 
-        segments = [{ 'from': segment[0], 'to': segment[1] } for segment in predicted['segments']]
+        segments = [{ 'from': segment[0], 'to': segment[1] } for segment in detected['segments']]
-        newCache = predicted['cache']
+        newCache = detected['cache']
 
         last_dataframe_time = dataframe.iloc[-1]['timestamp']
-        last_prediction_time = last_dataframe_time.value
+        last_detection_time = last_dataframe_time.value
         return {
             'cache': newCache,
             'segments': segments,
-            'lastPredictionTime': last_prediction_time
+            'lastDetectionTime': last_detection_time
         }

analytics/analytics/models/custom_model.py

@@ -7,5 +7,5 @@ class CustomModel(Model):
     def do_fit(self, dataframe: pd.DataFrame, segments: list) -> None:
         pass
 
-    def do_predict(self, dataframe: pd.DataFrame) -> list:
+    def do_detect(self, dataframe: pd.DataFrame) -> list:
         return []

analytics/analytics/models/drop_model.py

@@ -100,13 +100,13 @@ class DropModel(Model):
         else:
             self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
-    def do_predict(self, dataframe: pd.DataFrame) -> list:
+    def do_detect(self, dataframe: pd.DataFrame) -> list:
         data = dataframe['value']
         possible_drops = utils.find_drop(data, self.state['DROP_HEIGHT'], self.state['DROP_LENGTH'] + 1)
 
-        return self.__filter_prediction(possible_drops, data)
+        return self.__filter_detection(possible_drops, data)
 
-    def __filter_prediction(self, segments: list, data: list):
+    def __filter_detection(self, segments: list, data: list):
         delete_list = []
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)

analytics/analytics/models/general_model.py

@@ -78,7 +78,7 @@ class GeneralModel(Model):
         else:
             self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
-    def do_predict(self, dataframe: pd.DataFrame) -> list:
+    def do_detect(self, dataframe: pd.DataFrame) -> list:
         data = dataframe['value']
         pat_data = self.model_gen
         y = max(pat_data)
@@ -90,10 +90,10 @@ class GeneralModel(Model):
             self.all_conv.append(max(conv))
         all_conv_peaks = utils.peak_finder(self.all_conv, self.state['WINDOW_SIZE'] * 2)
 
-        filtered = self.__filter_prediction(all_conv_peaks, data)
+        filtered = self.__filter_detection(all_conv_peaks, data)
         return set(item + self.state['WINDOW_SIZE'] for item in filtered)
 
-    def __filter_prediction(self, segments: list, data: list):
+    def __filter_detection(self, segments: list, data: list):
         if len(segments) == 0 or len(self.ipats) == 0:
             return []
         delete_list = []

analytics/analytics/models/jump_model.py

@@ -102,13 +102,13 @@ class JumpModel(Model):
         else:
             self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
-    def do_predict(self, dataframe: pd.DataFrame) -> list:
+    def do_detect(self, dataframe: pd.DataFrame) -> list:
         data = dataframe['value']
         possible_jumps = utils.find_jump(data, self.state['JUMP_HEIGHT'], self.state['JUMP_LENGTH'] + 1)
 
-        return self.__filter_prediction(possible_jumps, data)
+        return self.__filter_detection(possible_jumps, data)
 
-    def __filter_prediction(self, segments, data):
+    def __filter_detection(self, segments, data):
         delete_list = []
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)

analytics/analytics/models/model.py

@@ -14,7 +14,7 @@ class Model(ABC):
         pass
 
     @abstractmethod
-    def do_predict(self, dataframe: pd.DataFrame) -> list:
+    def do_detect(self, dataframe: pd.DataFrame) -> list:
         pass
 
     def fit(self, dataframe: pd.DataFrame, segments: list, cache: Optional[AnalyticUnitCache]) -> AnalyticUnitCache:
@@ -34,11 +34,11 @@ class Model(ABC):
         self.do_fit(dataframe, segments)
         return self.state
 
-    def predict(self, dataframe: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
+    def detect(self, dataframe: pd.DataFrame, cache: Optional[AnalyticUnitCache]) -> dict:
         if type(cache) is AnalyticUnitCache:
             self.state = cache
 
-        result = self.do_predict(dataframe)
+        result = self.do_detect(dataframe)
         # TODO: convert from ns to ms more proper way (not dividing by 10^6)
         segments = [(
             dataframe['timestamp'][x - 1].value / 1000000,

analytics/analytics/models/peak_model.py

@@ -86,7 +86,7 @@ class PeakModel(Model):
         else:
             self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
-    def do_predict(self, dataframe: pd.DataFrame):
+    def do_detect(self, dataframe: pd.DataFrame):
         data = dataframe['value']
         window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
         all_maxs = argrelextrema(np.array(data), np.greater)[0]
@@ -100,9 +100,9 @@ class PeakModel(Model):
             if data[i] > extrema_list[i]:
                 segments.append(i)
 
-        return self.__filter_prediction(segments, data)
+        return self.__filter_detection(segments, data)
 
-    def __filter_prediction(self, segments: list, data: list) -> list:
+    def __filter_detection(self, segments: list, data: list) -> list:
         delete_list = []
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)

analytics/analytics/models/trough_model.py

@@ -87,7 +87,7 @@ class TroughModel(Model):
         else:
             self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
-    def do_predict(self, dataframe: pd.DataFrame):
+    def do_detect(self, dataframe: pd.DataFrame):
         data = dataframe['value']
         window_size = int(len(data)/SMOOTHING_COEFF) #test ws on flat data
         all_mins = argrelextrema(np.array(data), np.less)[0]
@@ -101,9 +101,9 @@ class TroughModel(Model):
             if data[i] < extrema_list[i]:
                 segments.append(i)
 
-        return self.__filter_prediction(segments, data)
+        return self.__filter_detection(segments, data)
 
-    def __filter_prediction(self, segments: list, data: list) -> list:
+    def __filter_detection(self, segments: list, data: list) -> list:
         delete_list = []
         variance_error = self.state['WINDOW_SIZE']
         close_patterns = utils.close_filtering(segments, variance_error)

server/src/controllers/alerts_controller.ts

@@ -5,7 +5,7 @@
 throw new Error('not supported');
 
 
-// import { runPredict } from './analytics_controller';
+// import { runDetect } from './analytics_controller';
 // import { getLabeledSegments } from './segments_controller';
 // import { AnalyticUnitId } from '../models/analytic_unit';
 // import { sendNotification } from '../services/notification_service';
@@ -56,10 +56,10 @@ throw new Error('not supported');
 
 // async function alertsTick() {
 //   let alertsAnomalies = getAlertsAnomalies();
-//   for (let predictorId of alertsAnomalies) {
+//   for (let detectorId of alertsAnomalies) {
 //     try {
-//       await runPredict(predictorId);
+//       await runDetect(detectorId);
-//       processAlerts(predictorId);
+//       processAlerts(detectorId);
 //     } catch (e) {
 //       console.error(e);
 //     }

server/src/controllers/analytics_controller.ts

@@ -51,7 +51,7 @@ async function onMessage(message: AnalyticsMessage) {
     methodResolved = true;
   }
 
-  if(message.method === AnalyticsMessageMethod.PREDICT) {
+  if(message.method === AnalyticsMessageMethod.DETECT) {
     onPredict(message.payload);
     methodResolved = true;
   }
@@ -159,7 +159,7 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
 
   try {
     let unit = await AnalyticUnit.findById(id);
-    previousLastPredictionTime = unit.lastPredictionTime;
+    previousLastPredictionTime = unit.lastDetectionTime;
     let pattern = unit.type;
 
     let segments = await Segment.findMany(id, { labeled: true });
@@ -183,8 +183,8 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
     }
     let task = new AnalyticsTask(
       id,
-      AnalyticsTaskType.PREDICT,
+      AnalyticsTaskType.DETECT,
-      { pattern, lastPredictionTime: unit.lastPredictionTime, data, cache: oldCache }
+      { pattern, lastPredictionTime: unit.lastDetectionTime, data, cache: oldCache }
     );
     console.debug(`run task, id:${id}`);
     let result = await runTask(task);
@@ -209,13 +209,13 @@ export async function runPredict(id: AnalyticUnit.AnalyticUnitId) {
 
     Segment.insertSegments(payload.segments);
     AnalyticUnitCache.setData(id, payload.cache);
-    AnalyticUnit.setPredictionTime(id, payload.lastPredictionTime);
+    AnalyticUnit.setDetectionTime(id, payload.lastPredictionTime);
     AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.READY);
   } catch(err) {
     let message = err.message || JSON.stringify(err);
     await AnalyticUnit.setStatus(id, AnalyticUnit.AnalyticUnitStatus.FAILED, message);
     if(previousLastPredictionTime !== undefined) {
-      await AnalyticUnit.setPredictionTime(id, previousLastPredictionTime);
+      await AnalyticUnit.setDetectionTime(id, previousLastPredictionTime);
     }
   }
 }

server/src/models/analytic_unit_model.ts

@@ -22,7 +22,7 @@ export class AnalyticUnit {
     public type: string,
     public metric: Metric,
     public id?: AnalyticUnitId,
-    public lastPredictionTime?: number,
+    public lastDetectionTime?: number,
     public status?: AnalyticUnitStatus,
     public error?: string,
   ) {
@@ -47,7 +47,7 @@ export class AnalyticUnit {
       panelUrl: this.panelUrl,
       type: this.type,
       metric: this.metric.toObject(),
-      lastPredictionTime: this.lastPredictionTime,
+      lastDetectionTime: this.lastDetectionTime,
       status: this.status,
       error: this.error
     };
@@ -63,7 +63,7 @@ export class AnalyticUnit {
       obj.type,
       Metric.fromObject(obj.metric),
       obj._id,
-      obj.lastPredictionTime,
+      obj.lastDetectionTime,
       obj.status as AnalyticUnitStatus,
       obj.error,
     );
@@ -106,6 +106,6 @@ export async function setStatus(id: AnalyticUnitId, status: string, error?: stri
   return db.updateOne(id, { status, error });
 }
 
-export async function setPredictionTime(id: AnalyticUnitId, lastPredictionTime: number) {
+export async function setDetectionTime(id: AnalyticUnitId, lastDetectionTime: number) {
-  return db.updateOne(id, { lastPredictionTime });
+  return db.updateOne(id, { lastDetectionTime });
 }

server/src/models/analytics_message_model.ts

@@ -1,7 +1,7 @@
 export enum AnalyticsMessageMethod {
   TASK = 'TASK',
   TASK_RESULT = 'TASK_RESULT',
-  PREDICT = 'PREDICT'
+  DETECT = 'DETECT'
 }
 
 export class AnalyticsMessage {
@@ -27,4 +27,4 @@ export class AnalyticsMessage {
     }
     return new AnalyticsMessage(obj.method, obj.payload, obj.requestId);
   }
-}
+}

server/src/models/analytics_task_model.ts

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