Add custom model

analytics/detectors/pattern_detector.py

@@ -23,6 +23,8 @@ def resolve_model_by_pattern(pattern: str) -> models.Model:
         return models.StepModel()
     if pattern == 'jump':
         return models.JumpModel()
+    if pattern == 'custom':
+        return models.CustomModel()
     raise ValueError('Unknown pattern "%s"' % pattern)
 
 

analytics/models/__init__.py

@@ -2,3 +2,4 @@ from models.model import Model
 from models.step_model import StepModel
 from models.peaks_model import PeaksModel
 from models.jump_model import JumpModel
+from models.custom_model import CustomModel

analytics/models/custom_model.py

@@ -0,0 +1,17 @@
+from models import Model
+import utils
+
+# Paste your model here:
+class CustomModel(Model):
+
+    def __init__(self):
+        super()
+        # Use self.state to store results of your learning
+        # It will be saved in filesystem and loaded after server restart
+        self.state = {}
+    
+    async def fit(self, dataframe, segments):
+        pass
+    
+    async def predict(self, dataframe):
+        return []