Anti-segments in general model #142 (#185)

analytics/models/drop_model.py

@@ -172,7 +172,7 @@ class DropModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if conv[self.state['WINDOW_SIZE']*2] > self.state['convolve_max'] * 1.2 or conv[self.state['WINDOW_SIZE']*2] < self.state['convolve_min'] * 0.8:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
                     delete_list.append(segment)
             else:
                 delete_list.append(segment)

analytics/models/general_model.py

@@ -23,6 +23,8 @@ class GeneralModel(Model):
             'convolve_max': 240,
             'convolve_min': 200,
             'WINDOW_SIZE': 240,
+            'conv_del_min': 100,
+            'conv_del_max': 120,
         }
         self.all_conv = []
 
@@ -38,7 +40,7 @@ class GeneralModel(Model):
                 segment_data = data[segment_from_index: segment_to_index + 1]
                 if len(segment_data) == 0:
                     continue
-                x = segment_from_index + int((segment_to_index - segment_from_index) / 2)
+                x = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2)
                 self.ipats.append(x)
                 segment_data = data[x - self.state['WINDOW_SIZE'] : x + self.state['WINDOW_SIZE']]
                 segment_min = min(segment_data)
@@ -53,6 +55,20 @@ class GeneralModel(Model):
             convolve_data = scipy.signal.fftconvolve(labeled_data, self.model_gen)
             convolve_list.append(max(auto_convolve))
             convolve_list.append(max(convolve_data))
+				
+        del_conv_list = []
+        for segment in segments:
+            if segment['deleted']:
+                segment_from_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['from'], unit='ms'))
+                segment_to_index = utils.timestamp_to_index(dataframe, pd.to_datetime(segment['to'], unit='ms'))
+                segment_data = data[segment_from_index: segment_to_index + 1]
+                if len(segment_data) == 0:
+                    continue
+                del_mid_index = segment_from_index + math.ceil((segment_to_index - segment_from_index) / 2)
+                deleted_pat = data[del_mid_index - self.state['WINDOW_SIZE']: del_mid_index + self.state['WINDOW_SIZE'] + 1]
+                deleted_pat = deleted_pat - min(deleted_pat)
+                del_conv_pat = scipy.signal.fftconvolve(deleted_pat, self.model_gen)
+                del_conv_list.append(max(del_conv_pat))
 
         if len(convolve_list) > 0:
             self.state['convolve_max'] = float(max(convolve_list))
@@ -63,6 +79,16 @@ class GeneralModel(Model):
             self.state['convolve_min'] = float(min(convolve_list))
         else:
             self.state['convolve_min'] = self.state['WINDOW_SIZE'] / 3
+		
+        if len(del_conv_list) > 0:
+            self.state['conv_del_min'] = float(min(del_conv_list))
+        else:
+            self.state['conv_del_min'] = self.state['WINDOW_SIZE']
+            
+        if len(del_conv_list) > 0:
+            self.state['conv_del_max'] = float(max(del_conv_list))
+        else:
+            self.state['conv_del_max'] = self.state['WINDOW_SIZE']
 
     def do_predict(self, dataframe: pd.DataFrame) -> list:
         data = dataframe['value']
@@ -88,6 +114,8 @@ class GeneralModel(Model):
         for val in segments:
             if self.all_conv[val] < self.state['convolve_min'] * 0.8:
                 delete_list.append(val)
+            elif (self.all_conv[val] < self.state['conv_del_max'] * 1.02 and self.all_conv[val] > self.state['conv_del_min'] * 0.98):
+	            delete_list.append(val)
 
         for item in delete_list:
             segments.remove(item)

analytics/models/jump_model.py

@@ -173,7 +173,7 @@ class JumpModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.2 or max(conv) < self.state['convolve_min'] * 0.8:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
                     delete_list.append(segment)
             else:
                 delete_list.append(segment)

analytics/models/peak_model.py

@@ -134,7 +134,7 @@ class PeakModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.05 or max(conv) < self.state['convolve_min'] * 0.95:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
                     delete_list.append(segment)
             else:
                 delete_list.append(segment)

analytics/models/trough_model.py

@@ -136,7 +136,7 @@ class TroughModel(Model):
                 conv = scipy.signal.fftconvolve(convol_data, pattern_data)
                 if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_min'] * 0.9:
                     delete_list.append(segment)
-                if max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
+                elif max(conv) < self.state['conv_del_max'] * 1.02 and max(conv) > self.state['conv_del_min'] * 0.98:
                     delete_list.append(segment)
             else:
                 delete_list.append(segment)