SacessOptimizer: expose more hyperparameters + minor fixes (#1459)

* Introduces a new SacessOptions class to collect SacessOptimizer hyperparameters and makes some previously hard-coded values configurable.

  Closes #1458

* Fixes a mismatch in adaptation conditions between the original SaCeSS implementation and SacessOptimizer (adaptation condition: AND -> OR)

* Log final worker configuration

* Sort refset before resizing.

pypesto/optimize/__init__.py

@@ -10,6 +10,7 @@
     ESSOptimizer,
     SacessFidesFactory,
     SacessOptimizer,
+    SacessOptions,
     get_default_ess_options,
 )
 from .load import (

pypesto/optimize/ess/__init__.py

@@ -10,5 +10,6 @@
 from .sacess import (
     SacessFidesFactory,
     SacessOptimizer,
+    SacessOptions,
     get_default_ess_options,
 )

pypesto/optimize/ess/refset.py

@@ -80,7 +80,7 @@ def initialize_random(
         self,
         n_diverse: int,
     ):
-        """Create initial reference set from random parameters.
+        """Create an initial reference set from random parameters.
 
         Sample ``n_diverse`` random points, populate half of the RefSet using
         the best solutions and fill the rest with random points.
@@ -90,7 +90,7 @@ def initialize_random(
         self.initialize_from_array(x_diverse=x_diverse, fx_diverse=fx_diverse)
 
     def initialize_from_array(self, x_diverse: np.array, fx_diverse: np.array):
-        """Create initial reference set using the provided points.
+        """Create an initial reference set using the provided points.
 
         Populate half of the RefSet using the best given solutions and fill the
         rest with a random selection from the remaining points.
@@ -174,7 +174,8 @@ def resize(self, new_dim: int):
         If the dimension does not change, do nothing.
         If size is decreased, drop entries from the end (i.e., the worst
         values, assuming it is sorted). If size is increased, the new
-        entries are filled with randomly and the refset is sorted.
+        entries are filled with randomly sampled parameters and the refset is
+        sorted.
 
         NOTE: Any attributes are just truncated or filled with zeros.
         """