Problem: add inner problem names, bounds and hierarchical flag

pypesto/hierarchical/base_problem.py

@@ -74,9 +74,9 @@ def get_interpretable_x_ids(self) -> list[str]:
 
         Interpretable parameters need to be easily interpretable by the user.
         Examples are scaling factors, offsets, or noise parameters. An example
-        for a non-interpretable inner parameters are spline heights of spline
-        approximation for semiquantitative data: it is hard to interpret what
-        the spline heights are just by looking at the parameter value.
+        of non-interpretable inner parameters is the spline heights of spline
+        approximation for semiquantitative data. It is challenging to interpret
+        the meaning of these parameters based solely on their value.
         """
         return list(self.xs.keys())
 

pypesto/hierarchical/inner_calculator_collector.py

@@ -248,7 +248,14 @@ def get_inner_par_ids(self) -> list[str]:
         ]
 
     def get_interpretable_inner_par_ids(self) -> list[str]:
-        """Return the ids of interpretable inner parameters of all inner problems."""
+        """Return the ids of interpretable inner parameters of all inner problems.
+
+        Interpretable parameters need to be easily interpretable by the user.
+        Examples are scaling factors, offsets, or noise parameters. An example
+        of non-interpretable inner parameters is the spline heights of spline
+        approximation for semiquantitative data. It is challenging to interpret
+        the meaning of these parameters based solely on their value.
+        """
         return [
             parameter_id
             for inner_calculator in self.inner_calculators

pypesto/petab/importer.py

@@ -45,7 +45,7 @@
 from ..objective.amici import AmiciObjectBuilder
 from ..objective.priors import NegLogParameterPriors, get_parameter_prior_dict
 from ..predict import AmiciPredictor
-from ..problem import Problem
+from ..problem import HierarchicalProblem, Problem
 from ..result import PredictionResult
 from ..startpoint import CheckedStartpoints, StartpointMethod
 
@@ -780,7 +780,12 @@ def create_problem(
                 )
             objective = AggregatedObjective([objective, prior])
 
-        problem = Problem(
+        if self._hierarchical:
+            problem_class = HierarchicalProblem
+        else:
+            problem_class = Problem
+
+        problem = problem_class(
             objective=objective,
             lb=lb,
             ub=ub,

pypesto/problem/__init__.py

@@ -8,3 +8,4 @@
 """
 
 from .base import Problem
+from .hierarchical import HierarchicalProblem

pypesto/problem/base.py

@@ -103,6 +103,7 @@ def __init__(
         ub_init: Union[np.ndarray, List[float], None] = None,
         copy_objective: bool = True,
         startpoint_method: Union[StartpointMethod, Callable, bool] = None,
+        hierarchical: bool = False,
     ):
         if copy_objective:
             objective = copy.deepcopy(objective)

pypesto/problem/hierarchical.py

@@ -0,0 +1,72 @@
+import logging
+from typing import Iterable, List, Optional, SupportsFloat, SupportsInt, Union
+
+import numpy as np
+
+from .base import Problem
+
+SupportsFloatIterableOrValue = Union[Iterable[SupportsFloat], SupportsFloat]
+SupportsIntIterableOrValue = Union[Iterable[SupportsInt], SupportsInt]
+
+logger = logging.getLogger(__name__)
+
+
+class HierarchicalProblem(Problem):
+    """
+    The Hierarchical Problem.
+
+    A hierarchical problem is a problem with a nested structure: One or
+    multiple inner problems are nested inside the outer problem. The inner
+    problems are optimized for each evaluation of the outer problem. The
+    objective's calculator is used to collect the inner problems' objective
+    values.
+
+    Parameters
+    ----------
+    hierarchical:
+        A flag indicating the problem is hierarchical.
+    inner_x_names:
+        Names of the inner optimization parameters. Only relevant if
+        hierarchical is True. Contains the names of easily interpretable
+        inner parameters only, e.g. noise parameters, scaling factors, offsets.
+    inner_lb, inner_ub:
+        The lower and upper bounds for the inner optimization parameters.
+        Only relevant if hierarchical is True. Contains the bounds of easily
+        interpretable inner parameters only, e.g. noise parameters, scaling
+        factors, offsets.
+    """
+
+    def __init__(
+        self,
+        inner_x_names: Optional[Iterable[str]] = None,
+        inner_lb: Optional[Union[np.ndarray, List[float]]] = None,
+        inner_ub: Optional[Union[np.ndarray, List[float]]] = None,
+        **problem_kwargs: dict,
+    ):
+        super().__init__(**problem_kwargs)
+
+        if inner_x_names is None:
+            inner_x_names = (
+                self.objective.calculator.get_interpretable_inner_par_ids()
+            )
+        if len(set(inner_x_names)) != len(inner_x_names):
+            raise ValueError("Parameter names inner_x_names must be unique")
+        self.inner_x_names = inner_x_names
+
+        if inner_lb is None or inner_ub is None:
+            (
+                default_inner_lb,
+                default_inner_ub,
+            ) = self.objective.calculator.get_interpretable_inner_par_bounds()
+            inner_lb = default_inner_lb if inner_lb is None else inner_lb
+            inner_ub = default_inner_ub if inner_ub is None else inner_ub
+
+        if len(inner_lb) != len(inner_ub):
+            raise ValueError("Parameter bounds must have same length")
+        if len(inner_lb) != len(inner_x_names):
+            raise ValueError(
+                "Parameter bounds must have same length as parameter names"
+            )
+
+        self.inner_lb = np.array(inner_lb)
+        self.inner_ub = np.array(inner_ub)

pypesto/visualize/parameters.py

@@ -462,26 +462,22 @@ def _handle_inner_inputs(
     inner_lb = None
     inner_ub = None
 
-    if any(inner_x is not None for inner_x in inner_xs):
-        from ..hierarchical import InnerCalculatorCollector
-
-        if hasattr(result.problem.objective, 'calculator') and isinstance(
-            inner_calculator := result.problem.objective.calculator,
-            InnerCalculatorCollector,
-        ):
-            inner_xs_names = inner_calculator.get_interpretable_inner_par_ids()
-            # replace None with a list of nans
-            inner_xs = [
-                np.full(len(inner_xs_names), np.nan)
-                if inner_xs_idx is None
-                else np.asarray(inner_xs_idx)
-                for inner_xs_idx in inner_xs
-            ]
-            # set bounds for inner parameters
-            (
-                inner_lb,
-                inner_ub,
-            ) = inner_calculator.get_interpretable_inner_par_bounds()
+    from ..problem import HierarchicalProblem
+
+    if any(inner_x is not None for inner_x in inner_xs) and isinstance(
+        result.problem, HierarchicalProblem
+    ):
+        inner_xs_names = result.problem.inner_x_names
+        # replace None with a list of nans
+        inner_xs = [
+            np.full(len(inner_xs_names), np.nan)
+            if inner_xs_idx is None
+            else np.asarray(inner_xs_idx)
+            for inner_xs_idx in inner_xs
+        ]
+        # set bounds for inner parameters
+        inner_lb = result.problem.inner_lb
+        inner_ub = result.problem.inner_ub
 
     if inner_xs_names is None:
         inner_xs = None