Added detailed log statements for model loading

Changed print statements to logging.debug() to ensure these are visible easily and in sequence with other logging statements in Cloudwatch logs.

Added more information including:
- length of predictions
- failed assertion message in case of multiple users for same trip list

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