Make models sync

analytics/detectors/pattern_detector.py

@@ -62,7 +62,7 @@ class PatternDetector:
 
         segments = self.data_prov.transform_anomalies(segments)
         # TODO: pass only part of dataframe that has segments
-        await self.model.fit(dataframe, segments)
+        self.model.fit(dataframe, segments)
         self.__save_model()
         return 0
 
@@ -77,7 +77,7 @@ class PatternDetector:
         start_index = max(0, start_index - window_size)
         dataframe = self.data_prov.get_data_range(start_index)
 
-        predicted_indexes = await self.model.predict(dataframe)
+        predicted_indexes = self.model.predict(dataframe)
         predicted_indexes = [(x, y) for (x, y) in predicted_indexes if x >= start_index and y >= start_index]
 
         predicted_times = self.data_prov.inverse_transform_indexes(predicted_indexes)

analytics/models/custom_model.py

@@ -10,8 +10,8 @@ class CustomModel(Model):
         # It will be saved in filesystem and loaded after server restart
         self.state = {}
     
-    async def fit(self, dataframe, segments):
+    def fit(self, dataframe, segments):
         pass
     
-    async def predict(self, dataframe):
+    def predict(self, dataframe):
         return []

analytics/models/jump_model.py

@@ -19,7 +19,7 @@ class JumpModel(Model):
             'convolve_max': WINDOW_SIZE
         }
     
-    async def fit(self, dataframe, segments):
+    def fit(self, dataframe, segments):
         self.segments = segments
         #self.alpha_finder()
         data = dataframe['value']
@@ -72,17 +72,17 @@ class JumpModel(Model):
         else:
             self.state['convolve_max'] = WINDOW_SIZE # макс метрика свертки равна отступу(WINDOW_SIZE), вау!
     
-    async def predict(self, dataframe):
+    def predict(self, dataframe):
         data = dataframe['value']
 
-        result = await self.__predict(data)
+        result = self.__predict(data)
         result.sort()
 
         if len(self.segments) > 0:
             result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
         return result
 
-    async def __predict(self, data):
+    def __predict(self, data):
         window_size = 24
         all_max_flatten_data = data.rolling(window=window_size).mean()
         all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]        

analytics/models/model.py

@@ -14,11 +14,11 @@ class Model(ABC):
         self.segments = []
 
     @abstractmethod
-    async def fit(self, dataframe: DataFrame, segments: list):
+    def fit(self, dataframe: DataFrame, segments: list):
         pass
 
     @abstractmethod
-    async def predict(self, dataframe: DataFrame) -> list:
+    def predict(self, dataframe: DataFrame) -> list:
         pass
 
     def save(self, model_filename: str):

analytics/models/peaks_model.py

@@ -10,10 +10,10 @@ class PeaksModel(Model):
     def __init__(self):
         super()
 
-    async def fit(self, dataset, contamination=0.005):
+    def fit(self, dataset, contamination=0.005):
         pass
 
-    async def predict(self, dataframe):
+    def predict(self, dataframe):
         array = dataframe['value'].as_matrix()
         window_size = 20
         # window = np.ones(101)

analytics/models/step_model.py

@@ -19,7 +19,7 @@ class StepModel(Model):
             'convolve_max': 570000
         }
 
-    async def fit(self, dataframe, segments):
+    def fit(self, dataframe, segments):
         self.segments = segments
         data = dataframe['value']
         confidences = []
@@ -47,17 +47,17 @@ class StepModel(Model):
         else:
             self.state['convolve_max'] = 570000
 
-    async def predict(self, dataframe):
+    def predict(self, dataframe):
         data = dataframe['value']
 
-        result = await self.__predict(data)
+        result = self.__predict(data)
         result.sort()
 
         if len(self.segments) > 0:
             result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
         return result
 
-    async def __predict(self, data):
+    def __predict(self, data):
         window_size = 24
         all_max_flatten_data = data.rolling(window=window_size).mean()
         all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]