Source code for pybop.optimisers.base_optimiser
from dataclasses import dataclass
from pybop._logging import Logger
from pybop._result import OptimisationResult
from pybop.problems.problem import Problem
@dataclass
[docs]
class OptimiserOptions:
"""
A base class for optimiser options.
Attributes
----------
multistart : int
Number of times to multistart the optimiser.
verbose : bool
The verbosity level.
verbose_print_rate : int
The distance between iterations to print verbose output.
"""
[docs]
verbose_print_rate: int = 50
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def validate(self):
"""
Validate the options.
Raises
------
ValueError
If the options are invalid.
"""
if self.multistart < 1:
raise ValueError("Multistart must be greater than or equal to 1.")
if self.verbose_print_rate < 1:
raise ValueError("Verbose print rate must be greater than or equal to 1.")
[docs]
class BaseOptimiser:
"""
A base class for defining optimisation methods.
This class serves as a base class for creating optimisers. It provides a basic structure for
an optimisation algorithm, including the initial setup and a method stub for performing the
optimisation process. Child classes should override _set_up_optimiser and the _run method with
a specific algorithm.
Parameters
----------
problem : pybop.Problem
The problem to optimise.
options: pybop.OptimiserOptions , optional
Options for the optimiser, such as multistart.
"""
[docs]
default_max_iterations = 1000
def __init__(
self,
problem: Problem,
options: OptimiserOptions | None = None,
):
if not isinstance(problem, Problem):
raise TypeError(f"Expected a pybop.Problem instance, got {type(problem)}")
[docs]
self._problem = problem
options = options or self.default_options()
options.validate()
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self._options = options
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self.verbose = options.verbose
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self.verbose_print_rate = options.verbose_print_rate
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self._multistart = options.multistart
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self._needs_sensitivities = None # to be overridden during set_up_optimiser
self._set_up_optimiser()
if self._needs_sensitivities and not self._problem.has_sensitivities:
raise ValueError(
"This optimiser needs sensitivities, but they are not available from this problem."
)
@staticmethod
[docs]
def default_options() -> OptimiserOptions:
"""Returns the default options for the optimiser."""
return OptimiserOptions()
@property
[docs]
def problem(self) -> Problem:
"""Returns the optimisation problem object."""
return self._problem
@property
[docs]
def options(self) -> OptimiserOptions:
"""Returns the options for the optimiser."""
return self._options
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def _set_up_optimiser(self):
"""
Parse optimiser options and prepare the optimiser.
This method should be implemented by child classes.
Raises
------
NotImplementedError
If the method has not been implemented by the subclass.
"""
raise NotImplementedError
[docs]
def _run(self) -> OptimisationResult:
"""
Contains the logic for the optimisation algorithm.
This method should be implemented by child classes to perform the actual optimisation.
Raises
------
NotImplementedError
If the method has not been implemented by the subclass.
"""
raise NotImplementedError
[docs]
def name(self) -> str:
"""
Returns the name of the optimiser, to be overwritten by child classes.
Returns
-------
str
The name of the optimiser
"""
raise NotImplementedError # pragma: no cover
[docs]
def run(self) -> OptimisationResult:
"""
Run the optimisation and return the optimised parameters and final cost.
Returns
-------
results: OptimisationResult
The pybop optimisation result class.
"""
results = []
for i in range(self._multistart):
if i >= 1:
if not self.problem.parameters.priors():
raise RuntimeError("Priors must be provided for multi-start")
initial_values = self.problem.parameters.sample_from_priors(1)[0]
self.problem.parameters.update(initial_values=initial_values)
self._set_up_optimiser()
results.append(self._run())
result = OptimisationResult.combine(results)
if self.options.verbose:
print(result)
return result
@property
[docs]
def logger(self) -> Logger | None:
return self._logger