Merge pull request #955 from MukuFlash03/log-statements-model-load

Cleanup fixes including log statements for PR #944

emission/analysis/classification/inference/labels/inferrers.py

@@ -171,14 +171,19 @@ def predict_cluster_confidence_discounting(trip_list, max_confidence=None, first
     user_id_list = []
     for trip in trip_list:
         user_id_list.append(trip['user_id'])
-    assert user_id_list.count(user_id_list[0]) == len(user_id_list), "Multiple user_ids found for trip_list, expected unique user_id for all trips"
+    error_message = f"""
+            Multiple user_ids found for trip_list, expected unique user_id for all trips.
+            Unique user_ids count = {len(set(user_id_list))}
+            {set(user_id_list)}
+        """
+    assert user_id_list.count(user_id_list[0]) == len(user_id_list), error_message
     # Assertion successful, use unique user_id
     user_id = user_id_list[0]
 
     # load model
     start_model_load_time = time.process_time()
     model = eamur._load_stored_trip_model(user_id, model_type, model_storage)
-    print(f"{arrow.now()} Inside predict_labels_n: Model load time = {time.process_time() - start_model_load_time}")
+    logging.debug(f"{arrow.now()} Inside predict_cluster_confidence_discounting: Model load time = {time.process_time() - start_model_load_time}")
 
     labels_n_list = eamur.predict_labels_with_n(trip_list, model)
     predictions_list = []
@@ -192,4 +197,4 @@ def predict_cluster_confidence_discounting(trip_list, max_confidence=None, first
         labels = copy.deepcopy(labels)
         for l in labels: l["p"] *= confidence_coeff
         predictions_list.append(labels)
-    return predictions_list
+    return predictions_list

emission/analysis/modelling/trip_model/run_model.py

@@ -109,7 +109,7 @@ def predict_labels_with_n(
     """
 
     predictions_list = []
-    print(f"{arrow.now()} Inside predict_labels_n: Predicting...")
+    logging.debug(f"{arrow.now()} Inside predict_labels_n: Predicting...")
     start_predict_time = time.process_time()
     for trip in trip_list:
         if model is None:
@@ -118,7 +118,8 @@ def predict_labels_with_n(
         else:
             predictions, n = model.predict(trip)
             predictions_list.append((predictions, n))
-    print(f"{arrow.now()} Inside predict_labels_n: Predictions complete for trip_list in time = {time.process_time() - start_predict_time}")
+    logging.debug(f"{arrow.now()} Inside predict_labels_n: Predictions complete for trip_list in time = {time.process_time() - start_predict_time}")
+    logging.debug(f"{arrow.now()} No. of trips = {len(trip_list)}; No. of predictions = {len(predictions_list)}")
     return predictions_list