Moved model loading step out of predict_labels_with_n

Refactored code to pass in the trip model directly to predict_labels_with_n() in eamur.

Moved the load model step to eacil.inferrers by using load_model() of eamur.

Modified TestRunGreedyModel to use this refactored function.

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

@@ -4,6 +4,8 @@
 import logging
 import random
 import copy
+import time
+import arrow
 
 import emission.analysis.modelling.tour_model_first_only.load_predict as lp
 import emission.analysis.modelling.trip_model.run_model as eamur
@@ -164,7 +166,21 @@ def predict_cluster_confidence_discounting(trip_list, max_confidence=None, first
     # load application config 
     model_type = eamtc.get_model_type()
     model_storage = eamtc.get_model_storage()
-    labels_n_list = eamur.predict_labels_with_n(trip_list, model_type, model_storage)
+
+    # assert and fetch unique user id for trip_list
+    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"
+    # 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}")
+
+    labels_n_list = eamur.predict_labels_with_n(trip_list, model)
     predictions_list = []
     for labels, n in labels_n_list:
         if n <= 0:  # No model data or trip didn't match a cluster

emission/analysis/modelling/trip_model/run_model.py

@@ -99,29 +99,15 @@ def update_trip_model(
 
 def predict_labels_with_n(
     trip_list: List[ecwc.Confirmedtrip],
-    model_type = eamumt.ModelType.GREEDY_SIMILARITY_BINNING,
-    model_storage = eamums.ModelStorage.DOCUMENT_DATABASE,
-    model_config = None):
+    model: eamuu.TripModel):
     """
     invoke the user label prediction model to predict labels for a trip.
 
     :param trip_list: the list of trips to predict labels for
-    :param model_type: type of prediction model to run
-    :param model_storage: location to read/write models
-    :param model_config: optional configuration for model, for debugging purposes
+    :param model: trip model used for predictions
     :return: a list of predictions
     """
 
-    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"
-    # Assertion successful, use unique user_id
-    user_id = user_id_list[0]
-
-    start_model_load_time = time.process_time()
-    model = _load_stored_trip_model(user_id, model_type, model_storage, model_config)
-    print(f"{arrow.now()} Inside predict_labels_n: Model load time = {time.process_time() - start_model_load_time}")
     predictions_list = []
     print(f"{arrow.now()} Inside predict_labels_n: Predicting...")
     start_predict_time = time.process_time()

emission/tests/modellingTests/TestRunGreedyModel.py

@@ -163,13 +163,18 @@ def test1RoundTripGreedySimilarityBinning(self):
             origin=self.origin,
             destination=self.destination
         )
-        
-        predictions_list = eamur.predict_labels_with_n(
-            trip_list = [test],
+
+        model = eamur._load_stored_trip_model(
+            user_id, 
             model_type=eamumt.ModelType.GREEDY_SIMILARITY_BINNING,
             model_storage=eamums.ModelStorage.DOCUMENT_DATABASE,
             model_config=greedy_model_config
         )
+
+        predictions_list = eamur.predict_labels_with_n(
+            trip_list = [test],
+            model = model
+        )
 
         for prediction, n in predictions_list:
             [logging.debug(p) for p in sorted(prediction, key=lambda r: r['p'], reverse=True)]