Regression prediction

src/data-hub/proactive-community-detection-microservice/app/db/repository.py

@@ -132,6 +132,10 @@ class Repository(MongoRepositoryBase):
         entries = super().get_entries(self._prediction_result_collection, selection={'use_case': use_case}, projection={'_id': 0})
         return [PredictionResult.create_from_dict(e) for e in entries]
 
+    def get_prediction_results_for_layer(self, use_case: str, use_case_table: str, layer_name: str) -> List[PredictionResult]:
+        entries = super().get_entries(self._prediction_result_collection, selection={'use_case': use_case, 'table': use_case_table, 'layer': layer_name}, projection={'_id': 0})
+        return [PredictionResult.create_from_dict(e) for e in entries]
+
     def delete_all_prediction_results(self):
         super().drop_collection(self._prediction_result_collection)
 #endregion

src/data-hub/proactive-community-detection-microservice/app/processing/ml/predict_cross_context.py

@@ -91,7 +91,8 @@ def run_prediction(use_case: str):
             prediction_metrics.append(flat_)
     
         prediction_results = svc.predict(scaler.transform(np.array(prediction_metrics)))
-        print(np.unique(prediction_results, return_counts=True))
+        # print(np.unique(prediction_results, return_counts=True))
+        prediction_results = np.rint(prediction_results) # round to full numbers
 
         for i in range(len(prediction_cluster_ids)):
             res = PredictionResult(use_case, use_case, method, layer_name, reference_layer_name, prediction_cluster_ids[i], prediction_time_window, prediction_results[i])

src/data-hub/proactive-community-detection-microservice/app/processing/ml/predict_single_context.py

@@ -74,7 +74,8 @@ def run_prediction(use_case: str):
             
         # predict all at once for speedup
         prediction_results = svc.predict(scaler.transform(np.array(prediction_metrics)))
-        print(np.unique(prediction_results, return_counts=True))
+        # print(np.unique(prediction_results, return_counts=True))
+        prediction_results = np.rint(prediction_results) # round to full numbers
         
         for i in range(len(prediction_cluster_ids)):
             res = PredictionResult(use_case, use_case, method, layer_name, None, prediction_cluster_ids[i], prediction_time_windows[i], prediction_results[i])

src/data-hub/proactive-community-detection-microservice/app/routes/predictions.py

@@ -5,7 +5,7 @@ from db.dao import PredictionResult
 repo = Repository()
 
 def get(use_case, table, layer_name):
-    res = repo.get_prediction_results(use_case)
+    res = repo.get_prediction_results_for_layer(use_case, table, layer_name)
     if res is None or len(res) == 0:
         return Response(status=404)
     else: