can now skip persi reference if not provided in competitor list

mlair/configuration/defaults.py

@@ -48,7 +48,7 @@ DEFAULT_TEST_END = "2017-12-31"
 DEFAULT_TEST_MIN_LENGTH = 90
 DEFAULT_TRAIN_VAL_MIN_LENGTH = 180
 DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS = True
-DEFAULT_COMPETITORS = ["ols"]
+DEFAULT_COMPETITORS = ["ols", "persi"]
 DEFAULT_DO_UNCERTAINTY_ESTIMATE = True
 DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH = "1m"
 DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS = True

mlair/run_modules/post_processing.py

@@ -72,6 +72,7 @@ class PostProcessing(RunEnvironment):
         self.model: AbstractModelClass = self._load_model()
         self.model_name = self.data_store.get("model_name", "model").rsplit("/", 1)[1].split(".", 1)[0]
         self.ols_model = None
+        self.persi_model = True
         self.batch_size: int = self.data_store.get_default("batch_size", "model", 64)
         self.test_data = self.data_store.get("data_collection", "test")
         batch_path = self.data_store.get("batch_path", scope="test")
@@ -106,6 +107,9 @@ class PostProcessing(RunEnvironment):
         # ols model
         self.train_ols_model()
 
+        # persi model
+        self.setup_persistence()
+
         # forecasts on test data
         self.make_prediction(self.test_data)
         self.make_prediction(self.train_val_data)
@@ -715,6 +719,12 @@ class PostProcessing(RunEnvironment):
         else:
             logging.info(f"Skip train ols model as it is not present in competitors.")
 
+    def setup_persistence(self):
+        """Check if persistence is requested from competitors and store this information."""
+        self.persi_model = any(x in map(str.lower, self.competitors) for x in ["persi", "persistence"])
+        if self.persi_model is False:
+            logging.info(f"Persistence is not calculated as it is not present in competitors.")
+
     @TimeTrackingWrapper
     def make_prediction(self, subset):
         """
@@ -748,8 +758,11 @@ class PostProcessing(RunEnvironment):
                 nn_prediction = self._create_nn_forecast(copy.deepcopy(nn_output), nn_prediction, transformation_func, normalised)
 
                 # persistence
+                if self.persi_model is True:
                     persistence_prediction = self._create_persistence_forecast(observation_data, persistence_prediction,
                                                                                transformation_func, normalised)
+                else:
+                    persistence_prediction = None
 
                 # ols
                 if self.ols_model is not None: