Select: problem-specific minimize method for SaCeSS

pypesto/select/__init__.py

@@ -7,7 +7,7 @@
 """
 
 from . import postprocessors
-from .misc import model_to_pypesto_problem
+from .misc import SacessMinimizeMethod, model_to_pypesto_problem
 from .problem import Problem
 
 try:

pypesto/select/method.py

@@ -245,6 +245,7 @@ def __init__(
         # TODO deprecated
         model_to_pypesto_problem_method: Callable[[Any], Problem] = None,
         model_problem_options: dict = None,
+        previously_calibrated_models=None,
     ):
         """Arguments are used in every `__call__`, unless overridden."""
         self.petab_select_problem = petab_select_problem
@@ -256,6 +257,10 @@ def __init__(
         self.select_first_improvement = select_first_improvement
         self.startpoint_latest_mle = startpoint_latest_mle
 
+        self.previously_calibrated_models = {}
+        if previously_calibrated_models is not None:
+            self.previously_calibrated_models = previously_calibrated_models
+
         self.logger = MethodLogger()
 
         # TODO deprecated
@@ -384,8 +389,19 @@ def __call__(
         #      `self.select_first_improvement`)
         newly_calibrated_models = {}
         for candidate_model in candidate_space.models:
-            # autoruns calibration
-            self.new_model_problem(model=candidate_model)
+            # If the user has previously calibrated this model,
+            # skip calibration.
+            if candidate_model.get_hash() in self.previously_calibrated_models:
+                _model_problem = self.new_model_problem(
+                    model=candidate_model,
+                    autorun=False,
+                )
+                _model_problem.set_result_from_model(
+                    self.previously_calibrated_models[candidate_model.get_hash()]
+                )
+            else:
+                self.new_model_problem(model=candidate_model)
+
             newly_calibrated_models[
                 candidate_model.get_hash()
             ] = candidate_model

pypesto/select/misc.py

@@ -11,6 +11,10 @@
 from petab_select.constants import PETAB_PROBLEM
 
 from ..objective import Objective
+from ..optimize.ess import (
+    SacessOptimizer,
+    get_default_ess_options,
+)
 from ..petab import PetabImporter
 from ..problem import Problem
 
@@ -163,3 +167,51 @@ def correct_x_guesses(
                 corrected_x_guess.append(corrected_value)
             corrected_x_guesses.append(corrected_x_guess)
     return corrected_x_guesses
+
+
+class SacessMinimizeMethod:
+    """Create a minimize method for SaCeSS that adapts to each problem.
+
+    When a pyPESTO SaCeSS optimizer is created, it takes the problem
+    dimension as input. Hence, an optimizer needs to be constructed for
+    each problem. Objects of this class act like a minimize method for model
+    selection, but a new problem-specific SaCeSS optimizer will be created
+    every time a model is minimized.
+
+    Class attributes correspond to pyPESTO's SaCeSS optimizer, and are
+    documented there.
+    """
+
+    def __init__(
+        self,
+        num_workers: int,
+        local_optimizer,
+        max_walltime_s: int,
+    ):
+        """Construct a minimize-like object."""
+        self.num_workers = num_workers
+        self.local_optimizer = local_optimizer
+        self.max_walltime_s = max_walltime_s
+
+    def __call__(self, problem: Problem, **minimize_options):
+        """Create then run a problem-specific sacess optimizer."""
+        # create optimizer
+        ess_init_args = get_default_ess_options(
+            num_workers=self.num_workers,
+            dim=problem.dim,
+        )
+        for x in ess_init_args:
+            x["local_optimizer"] = self.local_optimizer
+        ess = SacessOptimizer(
+            max_walltime_s=self.max_walltime_s,
+            sacess_loglevel=logging.DEBUG,
+            ess_loglevel=logging.WARNING,
+            ess_init_args=ess_init_args,
+        )
+
+        # optimize
+        result = ess.minimize(
+            problem=problem,
+            **minimize_options,
+        )
+        return result

pypesto/select/model_problem.py

@@ -150,6 +150,12 @@ def minimize(self) -> Result:
             **self.minimize_options,
         )
 
+    def set_result_from_model(self, model):
+        self.model.criteria = model.criteria
+        self.model.estimated_parameters = model.estimated_parameters
+        if self.postprocessor is not None:
+            self.postprocessor(self)
+
     def set_result(self, result: Result):
         """Postprocess a result.