Address Copilot review: fix AttributeError, assert→RuntimeError, print→logger, typo

- StaticRecommendationExperiment: initialize preproc_rec_params in all
  __init__ paths to prevent AttributeError in evaluate()
- Replace assert statements with RuntimeError for interp_results checks
  (asserts are stripped with python -O)
- Replace print() calls with logger.info() in stochastic_benchmark.py
- Fix typo: overriden → overridden

Author: bernalde

src/experiments.py

@@ -60,7 +60,7 @@ class Experiment:
 
     def evaluate(self):
         raise NotImplementedError(
-            "Evaluate should be overriden by a subclass of Experiment"
+            "Evaluate should be overridden by a subclass of Experiment"
         )
 
     def evaluate_monotone(self):
@@ -483,9 +483,12 @@ def __init__(self, parent_params, init_from):
             self.rec_params = init_from.recipe
             if init_from.postprocess is not None:
                 self.preproc_rec_params = init_from.preproc_recipe.copy()
+            else:
+                self.preproc_rec_params = init_from.recipe.copy()
 
         elif type(init_from) == pd.DataFrame:
             self.rec_params = init_from
+            self.preproc_rec_params = init_from.copy()
         else:
             warn_str = (
                 "init_from type is not supported. No recommended parameters are set."

src/stochastic_benchmark.py

@@ -271,7 +271,7 @@ def run_Bootstrap(self, bsParams_iter, group_name_fcn=None):
             logger.info("Bootstrapped results is already populated: doing nothing.")
             return
 
-        print("Loading and bootstrapping experimental data...")
+        logger.info("Loading and bootstrapping experimental data...")
         if self.reduce_mem:
             self.raw_data = glob.glob(os.path.join(self.here.raw_data, "*.pkl"))
 
@@ -394,7 +394,7 @@ def run_Interpolate(self, iParams):
                 "Ensure Bootstrap() or initBootstrap() has been called successfully."
             )
 
-        print("Interpolating results across resource budgets...")
+        logger.info("Interpolating results across resource budgets...")
         if self.reduce_mem:
             logger.info("Interpolating results with parameters: %s", iParams)
             if not isinstance(self.bs_results, list):
@@ -414,7 +414,8 @@ def run_Interpolate(self, iParams):
                 self.bs_results, iParams, self.parameter_names + self.instance_cols
             )
 
-        assert self.interp_results is not None, "Interpolation failed to produce results"
+        if self.interp_results is None:
+            raise RuntimeError("Interpolation failed to produce results")
 
         base = names.param2filename({"Key": self.response_key}, "")
         CIlower = names.param2filename(
@@ -436,14 +437,15 @@ def run_Stats(self, stat_params, train_test_split=0.5):
                 "Interpolated results needs to be populated before computing stats."
             )
 
-        print("Computing training/testing statistics...")
+        logger.info("Computing training/testing statistics...")
         if "train" not in self.interp_results.columns:
             self.interp_results = training.split_train_test(
                 self.interp_results, self.instance_cols, train_test_split
             )
             self.interp_results.to_pickle(self.here.interpolate)
 
-        assert self.interp_results is not None, "interp_results was unexpectedly None"
+        if self.interp_results is None:
+            raise RuntimeError("interp_results was unexpectedly None")
 
         if self.training_stats is None:
             if os.path.exists(self.here.training_stats) and self.recover:
@@ -566,7 +568,8 @@ def populate_interp_results(self):
                         self.parameter_names + self.instance_cols,
                     )
 
-                assert self.interp_results is not None, "Interpolation failed to produce results"
+                if self.interp_results is None:
+                    raise RuntimeError("Interpolation failed to produce results")
 
                 base = names.param2filename({"Key": self.response_key}, "")
                 CIlower = names.param2filename(
@@ -640,8 +643,7 @@ def run_baseline(self):
         """
         Adds virtual best baseline
         """
-        print("Computing virtual best baseline...")
-        logger.info("Runnng baseline")
+        logger.info("Computing virtual best baseline...")
         self.baseline = VirtualBestBaseline(self.get_experiment_parameters())
 
     def run_ProjectionExperiment(
@@ -664,8 +666,7 @@ def run_ProjectionExperiment(
         ProjectionExperiment
             Experiment object
         """
-        print(f"  ├─ Running ProjectionExperiment from {project_from}...")
-        logger.info("Running projection experiment")
+        logger.info("Running ProjectionExperiment from %s...", project_from)
         self.experiments.append(
             ProjectionExperiment(self.get_experiment_parameters(), project_from, postprocess, postprocess_name)
         )
@@ -676,8 +677,7 @@ def run_RandomSearchExperiment(
         """
         Runs random search experiments
         """
-        print("  ├─ Running RandomSearchExperiment...")
-        logger.info("Running random search experiment")
+        logger.info("Running RandomSearchExperiment...")
         self.experiments.append(
             RandomSearchExperiment(
                 self.get_experiment_parameters(),
@@ -710,8 +710,7 @@ def run_SequentialSearchExperiment(
             Experiment object
         """
         id_label = f" ({id_name})" if id_name else ""
-        print(f"  ├─ Running SequentialSearchExperiment{id_label}...")
-        logger.info("Running sequential search experiment")
+        logger.info("Running SequentialSearchExperiment%s...", id_label)
         self.experiments.append(
             SequentialSearchExperiment(
                 self.get_experiment_parameters(),