import logging
from typing import Any, Literal
import numpy as np
from ..C import (
DUMMY_INNER_VALUE,
INNER_PARAMETER_BOUNDS,
LIN,
LOG,
LOG10,
LOWER_BOUND,
UPPER_BOUND,
InnerParameterType,
)
logger = logging.getLogger(__name__)
[docs]class InnerParameter:
"""
An inner parameter of a hierarchical optimization problem.
Attributes
----------
coupled:
Whether the inner parameter is part of an observable that has both
an offset and scaling inner parameter.
dummy_value:
Value to be used when the optimal parameter is not yet known
(in particular to simulate unscaled observables).
inner_parameter_id:
The inner parameter ID.
inner_parameter_type:
The inner parameter type.
ixs:
A mask (boolean matrix) that indicates the measurements that this
parameter is used in.
lb:
The lower bound, for optimization.
scale:
Scale on which to estimate this parameter.
ub:
The upper bound, for optimization.
"""
[docs] def __init__(
self,
inner_parameter_id: str,
inner_parameter_type: InnerParameterType,
scale: Literal[LIN, LOG, LOG10] = LIN,
lb: float = -np.inf,
ub: float = np.inf,
ixs: Any = None,
dummy_value: float = None,
):
"""
Construct.
Parameters
----------
See class attributes.
"""
self.inner_parameter_id: str = inner_parameter_id
self.coupled = False
self.inner_parameter_type: str = inner_parameter_type
if scale not in {LIN, LOG, LOG10}:
raise ValueError(f"Scale not recognized: {scale}.")
self.scale = scale
if inner_parameter_type not in (
InnerParameterType.OPTIMAL_SCALING,
InnerParameterType.OFFSET,
InnerParameterType.SIGMA,
InnerParameterType.SCALING,
InnerParameterType.SPLINE,
):
raise ValueError(
f"Unsupported inner parameter type `{inner_parameter_type}`."
)
self.lb: float = lb
self.ub: float = ub
self.check_bounds()
self.ixs: Any = ixs
if dummy_value is None:
try:
dummy_value = DUMMY_INNER_VALUE[inner_parameter_type]
except KeyError as e:
raise ValueError(
"Unsupported parameter type. Parameter id:"
f"`{inner_parameter_id}`. Parameter type:"
f"`{inner_parameter_type}`."
) from e
self.dummy_value: float = dummy_value
[docs] def check_bounds(self):
"""Check bounds."""
expected_lb = INNER_PARAMETER_BOUNDS[self.inner_parameter_type][
LOWER_BOUND
]
expected_ub = INNER_PARAMETER_BOUNDS[self.inner_parameter_type][
UPPER_BOUND
]
if self.lb != expected_lb or self.ub != expected_ub:
raise ValueError(
"Invalid bounds for inner parameters. Parameter ID: "
f"`{self.inner_parameter_id}`. Provided bounds: "
f"`[{self.lb}, {self.ub}]`. Expected bounds: "
f"`[{expected_lb}, {expected_ub}]`. "
f"All expected parameter bounds:\n{INNER_PARAMETER_BOUNDS}"
)