Remove experimental argument

src/tranquilo/options.py

@@ -31,6 +31,10 @@ def get_default_batch_size(n_cores):
     return n_cores
 
 
+def get_default_filter_target_sample_size():
+    return 3
+
+
 def get_default_acceptance_decider(noisy):
     return "noisy" if noisy else "classic"
 

src/tranquilo/process_arguments.py

@@ -22,6 +22,7 @@
     get_default_sample_size,
     get_default_search_radius_factor,
     get_default_stagnation_options,
+    get_default_filter_target_sample_size,
     update_option_bundle,
     NoiseAdaptationOptions,
 )
@@ -87,9 +88,6 @@ def process_arguments(
     variance_estimator_options=None,
     infinity_handler="relative",
     residualize=None,
-    # Experimental options
-    experimental=None,
-    filter_target_sample_size_factor=None,
 ):
     # warning for things that do not work well yet
     if noisy and functype == "scalar":
@@ -167,6 +165,7 @@ def process_arguments(
         model_type=model_type,
         x=x,
     )
+    filter_target_sample_size = _process_filter_target_sample_size(target_sample_size)
     model_fitter = _process_model_fitter(
         model_fitter, model_type=model_type, sample_size=target_sample_size, x=x
     )
@@ -234,6 +233,7 @@ def process_arguments(
         "radius_options": radius_options,
         "batch_size": batch_size,
         "target_sample_size": target_sample_size,
+        "filter_target_sample_size": filter_target_sample_size,
         "stagnation_options": stagnation_options,
         "noise_adaptation_options": noise_adaptation_options,
         "search_radius_factor": search_radius_factor,
@@ -250,8 +250,6 @@ def process_arguments(
         "aggregate_model": aggregate_model,
         "estimate_variance": estimate_variance,
         "accept_candidate": accept_candidate,
-        "filter_target_sample_size_factor": filter_target_sample_size_factor,
-        "experimental": experimental,
     }
 
     return out
@@ -369,3 +367,8 @@ def _process_n_evals_per_point(n_evals, noisy, noise_adaptation_options):
         raise ValueError("n_evals_per_point must be non-negative.")
 
     return out
+
+
+def _process_filter_target_sample_size(target_sample_size):
+    factor = get_default_filter_target_sample_size()
+    return int(np.floor(target_sample_size * factor))

src/tranquilo/tranquilo.py

@@ -34,6 +34,7 @@ def _internal_tranquilo(
     noise_adaptation_options,
     batch_size,
     target_sample_size,
+    filter_target_sample_size,
     stagnation_options,
     search_radius_factor,
     n_evals_per_point,
@@ -49,9 +50,6 @@ def _internal_tranquilo(
     aggregate_model,
     estimate_variance,
     accept_candidate,
-    # experimental options
-    experimental,
-    filter_target_sample_size_factor,
 ):
     if n_evals_at_start > 1:
         eval_info = {0: next_multiple(n_evals_at_start, base=batch_size)}
@@ -108,18 +106,11 @@ def _internal_tranquilo(
 
         old_xs = history.get_xs(old_indices)
 
-        if experimental:
-            _filter_target_sample_size_factor = filter_target_sample_size_factor
-        else:
-            _filter_target_sample_size_factor = 1
-
         model_xs, model_indices = filter_points(
             xs=old_xs,
             indices=old_indices,
             state=state,
-            target_size=int(
-                np.floor(_filter_target_sample_size_factor * target_sample_size)
-            ),
+            target_size=filter_target_sample_size,
             history=history,
             n_evals_per_point=n_evals_per_point,
         )