Relative: Fix return of inner parameters on objective call

pypesto/hierarchical/inner_calculator_collector.py

@@ -112,7 +112,6 @@ def __init__(
 
     def initialize(self):
         """Initialize."""
-        self.best_fval = np.inf
         for calculator in self.inner_calculators:
             calculator.initialize()
 
@@ -125,7 +124,6 @@ def construct_inner_calculators(
     ):
         """Construct inner calculators for each data type."""
         self.necessary_par_dummy_values = {}
-        self.best_fval = np.inf
 
         if RELATIVE in self.data_types:
             relative_inner_problem = RelativeInnerProblem.from_petab_amici(
@@ -382,9 +380,6 @@ def __call__(
                 parameter_mapping=parameter_mapping,
                 fim_for_hess=fim_for_hess,
             )
-            # only return inner parameters if the objective value improved
-            if ret[FVAL] > self.best_fval:
-                ret[INNER_PARAMETERS] = None
             return filter_return_dict(ret)
 
         # get dimension of outer problem
@@ -517,16 +512,12 @@ def __call__(
             RDATAS: rdatas,
         }
 
-        # Add inner parameters to return dict
-        # only if the objective value improved.
-        if ret[FVAL] < self.best_fval:
-            ret[SPLINE_KNOTS] = spline_knots
-            ret[INNER_PARAMETERS] = (
-                interpretable_inner_pars
-                if len(interpretable_inner_pars) > 0
-                else None
-            )
-            self.best_fval = ret[FVAL]
+        ret[INNER_PARAMETERS] = (
+            interpretable_inner_pars
+            if len(interpretable_inner_pars) > 0
+            else None
+        )
+        ret[SPLINE_KNOTS] = spline_knots
 
         return filter_return_dict(ret)
 

pypesto/objective/amici/amici.py

@@ -10,11 +10,9 @@
 
 from ...C import (
     FVAL,
-    INNER_PARAMETERS,
     MODE_FUN,
     MODE_RES,
     RDATAS,
-    SPLINE_KNOTS,
     SUFFIXES_CSV,
     SUFFIXES_HDF5,
     ModeType,
@@ -233,10 +231,6 @@ def __init__(
         # `set_custom_timepoints` method for more information.
         self.custom_timepoints = None
 
-        # Initialize the list for saving of inner parameter values.
-        self.inner_parameters: list[float] = None
-        self.spline_knots: list[list[list[float]]] = None
-
     def get_config(self) -> dict:
         """Return basic information of the objective configuration."""
         info = super().get_config()
@@ -503,11 +497,6 @@ def call_unprocessed(
 
         nllh = ret[FVAL]
         rdatas = ret[RDATAS]
-        if ret.get(INNER_PARAMETERS, None) is not None:
-            self.inner_parameters = ret[INNER_PARAMETERS]
-
-        if ret.get(SPLINE_KNOTS, None) is not None:
-            self.spline_knots = ret[SPLINE_KNOTS]
 
         # check whether we should update data for preequilibration guesses
         if (

pypesto/optimize/optimizer.py

@@ -14,7 +14,7 @@
 from ..C import FVAL, GRAD, INNER_PARAMETERS, MODE_FUN, MODE_RES, SPLINE_KNOTS
 from ..history import HistoryOptions, NoHistory, OptimizerHistory
 from ..objective import Objective
-from ..problem import Problem
+from ..problem import HierarchicalProblem, Problem
 from ..result import OptimizerResult
 from .load import fill_result_from_history
 from .options import OptimizeOptions
@@ -61,18 +61,17 @@ def wrapped_minimize(
             optimize_options=optimize_options,
         )
 
-        # add inner parameters
-        if (
-            hasattr(problem.objective, INNER_PARAMETERS)
-            and problem.objective.inner_parameters is not None
-        ):
-            result[INNER_PARAMETERS] = problem.objective.inner_parameters
-
-        if (
-            hasattr(problem.objective, SPLINE_KNOTS)
-            and problem.objective.spline_knots is not None
-        ):
-            result[SPLINE_KNOTS] = problem.objective.spline_knots
+        if isinstance(problem, HierarchicalProblem):
+            # Call the objective to obtain inner parameters of
+            # the optimal outer optimization parameters
+            return_dict = problem.objective(
+                result.x,
+                return_dict=True,
+            )
+            if INNER_PARAMETERS in return_dict:
+                result[INNER_PARAMETERS] = return_dict[INNER_PARAMETERS]
+            if SPLINE_KNOTS in return_dict:
+                result[SPLINE_KNOTS] = return_dict[SPLINE_KNOTS]
 
         return result
 

pypesto/visualize/model_fit.py

@@ -17,9 +17,8 @@
 from petab.visualize import plot_problem
 
 from ..C import CENSORED, ORDINAL, RDATAS, SEMIQUANTITATIVE
-from ..hierarchical.relative.calculator import RelativeAmiciCalculator
 from ..petab.importer import get_petab_non_quantitative_data_types
-from ..problem import Problem
+from ..problem import HierarchicalProblem, Problem
 from ..result import Result
 from .ordinal_categories import plot_categories_from_pypesto_result
 from .spline_approximation import _add_spline_mapped_simulations_to_model_fit
@@ -241,7 +240,7 @@ def _get_simulation_rdatas(
     parameters = problem.get_reduced_vector(parameters)
 
     # simulate with custom timepoints for hierarchical model
-    if isinstance(problem.objective.calculator, RelativeAmiciCalculator):
+    if isinstance(problem, HierarchicalProblem):
         # get parameter dictionary
         x_dct = dict(
             zip(problem.x_names, result.optimize_result.list[start_index].x)
@@ -253,8 +252,10 @@ def _get_simulation_rdatas(
         )
 
         # update parameter dictionary with inner parameters
-        inner_parameters = ret["inner_parameters"]
-        x_dct.update(inner_parameters)
+        inner_parameter_dict = dict(
+            zip(problem.inner_x_names, ret["inner_parameters"])
+        )
+        x_dct.update(inner_parameter_dict)
 
         parameter_mapping = problem.objective.parameter_mapping
         edatas = copy.deepcopy(problem.objective.edatas)