import logging
from collections.abc import Iterable
from typing import 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__)
[docs]
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.
semiquant_observable_ids:
The ids of semiquantitative observables. Only relevant if hierarchical
is True. If not None, the optimization result's `spline_knots` will be
a list of lists of spline knots for each semiquantitative observable in
the order of these ids.
"""
[docs]
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)
self.semiquant_observable_ids = (
self.objective.calculator.semiquant_observable_ids
)