Apply suggestions from code review

Author: RemiLehe

optimas/gen_functions.py

@@ -96,7 +96,6 @@ def persistent_generator(H, persis_info, gen_specs, libE_info):
             # Check how many simulations have returned
             n = len(calc_in["sim_id"])
             # Feed the latest simulation results to the generator
-            trials = []
             for i in range(n):
                 trial_index = int(calc_in["trial_index"][i])
                 trial_status = calc_in["trial_status"][i]
@@ -109,10 +108,6 @@ def persistent_generator(H, persis_info, gen_specs, libE_info):
                         ev = Evaluation(parameter=par, value=y)
                         trial.complete_evaluation(ev)
                 generator.tell_trials([trial])
-                trials.append(trial)
-
-            # Register trials with unknown SEM
-            # generator.tell_trials(trials)  # Give as batch
 
             # Set the number of points to generate to that number:
             number_of_gen_points = min(n + n_failed_gens, max_evals - n_gens)

optimas/generators/ax/developer/multitask.py

@@ -196,17 +196,12 @@ def __init__(
         model_save_period: Optional[int] = 5,
         model_history_dir: Optional[str] = "model_history",
     ) -> None:
-        # SH note for standardization
         # As trial parameters these get written to history array
         # Ax trial_index and arm toegther locate a point
         # Multiple points (Optimas trials) can share the same Ax trial_index
-        # Standard (VOCS) does not have equiv. of trial parameters -> would be variables.
-        # If want to use _id can have a mapping inside the generator, but those
-        # points that are part of same ax trial_index will not be seen in history.
         custom_trial_parameters = [
             TrialParameter("arm_name", "ax_arm_name", dtype="U32"),
             TrialParameter("trial_type", "ax_trial_type", dtype="U32"),
-            # SH changed from trial_index to ax_trial_id - trial_index is usually Optimas index?
             TrialParameter("ax_trial_id", "ax_trial_index", dtype=int),
         ]
         self._check_inputs(varying_parameters, objectives, lofi_task, hifi_task)
@@ -289,7 +284,7 @@ def suggest(self, num_points: Optional[int]) -> List[dict]:
 
     def ingest(self, results: List[dict]) -> None:
         """Incorporate evaluated trials into experiment."""
-        # reconstruct Optimastrials
+        # reconstruct Optimas trials
         trials = []
         for result in results:
             trial = Trial.from_dict(