# cspell: ignore nfcn
"""Minuit2 adapter to the `iminuit.Minuit` package."""
import logging
import time
from typing import Callable, Iterable, Mapping, Optional
import iminuit
from tqdm.auto import tqdm
from tensorwaves.interface import (
Estimator,
FitResult,
Optimizer,
ParameterValue,
)
from ._parameter import ParameterFlattener
from .callbacks import Callback, _create_log
[docs]class Minuit2(Optimizer):
"""Adapter to `Minuit2 <https://root.cern.ch/doc/master/Minuit2Page.html>`_.
Implements the `~.interface.Optimizer` interface using `iminuit.Minuit`.
Args:
callback: Optionally insert behavior through :mod:`.callbacks` into
the :meth:`optimize` method.
use_analytic_gradient: Use the :meth:`.Estimator.gradient` when
calling :meth:`optimize`.
minuit_modifier: Modify the internal `iminuit.Minuit` optimizer that
is constructed during the :meth:`optimize` call. See
:ref:`usage/basics:Minuit2` for an example.
"""
def __init__(
self,
callback: Optional[Callback] = None,
use_analytic_gradient: bool = False,
minuit_modifier: Optional[Callable[[iminuit.Minuit], None]] = None,
) -> None:
self.__callback = callback
self.__use_gradient = use_analytic_gradient
if minuit_modifier is not None and not callable(minuit_modifier):
raise TypeError(
"minuit_modifier has to be a callable that takes a"
f" {iminuit.Minuit.__module__}.{iminuit.Minuit.__name__} "
"instance. See constructor signature."
)
self.__minuit_modifier = minuit_modifier
[docs] def optimize( # pylint: disable=too-many-locals
self,
estimator: Estimator,
initial_parameters: Mapping[str, ParameterValue],
) -> FitResult:
parameter_handler = ParameterFlattener(initial_parameters)
flattened_parameters = parameter_handler.flatten(initial_parameters)
progress_bar = tqdm(
disable=logging.getLogger().level > logging.WARNING
)
n_function_calls = 0
parameters = parameter_handler.unflatten(flattened_parameters)
if self.__callback is not None:
self.__callback.on_optimize_start(
logs=_create_log(
optimizer=type(self),
estimator_type=type(estimator),
estimator_value=estimator(parameters),
function_call=n_function_calls,
parameters=parameters,
)
)
def update_parameters(pars: list) -> None:
for i, k in enumerate(flattened_parameters):
flattened_parameters[k] = pars[i]
def wrapped_function(pars: list) -> float:
nonlocal n_function_calls
n_function_calls += 1
update_parameters(pars)
parameters = parameter_handler.unflatten(flattened_parameters)
estimator_value = float(estimator(parameters))
progress_bar.set_postfix({"estimator": estimator_value})
progress_bar.update()
if self.__callback is not None:
self.__callback.on_function_call_end(
n_function_calls,
logs=_create_log(
optimizer=type(self),
estimator_type=type(estimator),
estimator_value=estimator_value,
function_call=n_function_calls,
parameters=parameters,
),
)
return estimator_value
def wrapped_gradient(pars: list) -> Iterable[float]:
update_parameters(pars)
parameters = parameter_handler.unflatten(flattened_parameters)
grad = estimator.gradient(parameters)
return parameter_handler.flatten(grad).values()
minuit = iminuit.Minuit(
wrapped_function,
tuple(flattened_parameters.values()),
grad=wrapped_gradient if self.__use_gradient else None,
name=tuple(flattened_parameters),
)
minuit.errors = tuple(
0.1 * x if x != 0.0 else 0.1 for x in flattened_parameters.values()
)
minuit.errordef = (
iminuit.Minuit.LIKELIHOOD
) # that error definition should be defined in the estimator
if self.__minuit_modifier is not None:
self.__minuit_modifier(minuit)
start_time = time.time()
minuit.migrad()
end_time = time.time()
parameter_values = {}
parameter_errors = {}
for i, name in enumerate(flattened_parameters):
par_state = minuit.params[i]
parameter_values[name] = par_state.value
parameter_errors[name] = par_state.error
fit_result = FitResult(
minimum_valid=minuit.valid,
execution_time=end_time - start_time,
function_calls=minuit.fmin.nfcn,
estimator_value=minuit.fmin.fval,
parameter_values=parameter_handler.unflatten(parameter_values),
parameter_errors=parameter_handler.unflatten(parameter_errors),
specifics=minuit,
)
if self.__callback is not None:
self.__callback.on_optimize_end(
logs=_create_log(
optimizer=type(self),
estimator_type=type(estimator),
estimator_value=fit_result.estimator_value,
function_call=fit_result.function_calls,
parameters=fit_result.parameter_values,
)
)
return fit_result
