added logging messages and wrappers

mlair/run_modules/post_processing.py

@@ -128,6 +128,7 @@ class PostProcessing(RunEnvironment):
         with TimeTracking(name="calculate_error_metrics", log_on_enter=True):
             skill_score_competitive, _, skill_score_climatological, errors = self.calculate_error_metrics()
             self.skill_scores = (skill_score_competitive, skill_score_climatological)
+        with TimeTracking(name="report_error_metrics", log_on_enter=True):
             self.report_error_metrics(errors)
             self.report_error_metrics({self.forecast_indicator: skill_score_climatological})
             self.report_error_metrics({"skill_score": skill_score_competitive})
@@ -632,8 +633,10 @@ class PostProcessing(RunEnvironment):
             logging.error(f"Could not create plot PlotSampleUncertaintyFromBootstrap due to the following error: {e}"
                           f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
         
+    @TimeTrackingWrapper
     def calculate_test_score(self):
         """Evaluate test score of model and save locally."""
+        logging.info(f"start to calculate test scores")
 
         # test scores on transformed data
         test_score = self.model.evaluate(self.test_data_distributed,
@@ -644,8 +647,10 @@ class PostProcessing(RunEnvironment):
                 logging.info(f"{self.model.metrics_names[index]} (test), {item}")
                 f.write(f"{self.model.metrics_names[index]}, {item}\n")
 
+    @TimeTrackingWrapper
     def train_ols_model(self):
         """Train ordinary least squared model on train data."""
+        logging.info(f"start train_ols_model on train data")
         self.ols_model = OrdinaryLeastSquaredModel(self.train_data)
 
     def make_prediction(self, subset):