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Merged
Parent: Preparation for 2.0.0 release
merged 2 commits into from
Aug 7, 2024
Merged

minimizer consolidation #45

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6fd3d16
minimizer consolidation
andped10 Aug 6, 2024
7dea8b0
pr response
andped10 Aug 7, 2024
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91 changes: 91 additions & 0 deletions src/easyscience/fitting/minimizers/minimizer_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,9 @@

from abc import ABCMeta
from abc import abstractmethod
from inspect import Parameter as InspectParameter
from inspect import Signature
from inspect import _empty
from typing import Callable
from typing import Dict
from typing import List
Expand Down Expand Up @@ -166,6 +169,94 @@ def _prepare_parameters(self, parameters: dict[str, float]) -> dict[str, float]:
parameters[parameter_name] = item.raw_value
return parameters

def _generate_fit_function(self) -> Callable:
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@andped10 andped10 Aug 6, 2024 •
edited
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fetched from bumps / dfo

"""
Using the user supplied `fit_function`, wrap it in such a way we can update `Parameter` on
iterations.

:return: a fit function which is compatible with bumps models
"""
# Original fit function
func = self._original_fit_function
# Get a list of `Parameters`
self._cached_pars = {}
self._cached_pars_vals = {}
for parameter in self._object.get_fit_parameters():
key = parameter.unique_name
self._cached_pars[key] = parameter
self._cached_pars_vals[key] = (parameter.value, parameter.error)

# Make a new fit function
def _fit_function(x: np.ndarray, **kwargs):
"""
Wrapped fit function which now has an EasyScience compatible form

:param x: array of data points to be calculated
:type x: np.ndarray
:param kwargs: key word arguments
:return: points calculated at `x`
:rtype: np.ndarray
"""
# Update the `Parameter` values and the callback if needed
# TODO THIS IS NOT THREAD SAFE :-(
# TODO clean when full move to new_variable
from easyscience.Objects.new_variable import Parameter

for name, value in kwargs.items():
par_name = name[1:]
if par_name in self._cached_pars.keys():
# TODO clean when full move to new_variable
if isinstance(self._cached_pars[par_name], Parameter):
# This will take into account constraints
if self._cached_pars[par_name].value != value:
self._cached_pars[par_name].value = value
else:
# This will take into account constraints
if self._cached_pars[par_name].raw_value != value:
self._cached_pars[par_name].value = value

# Since we are calling the parameter fset will be called.
# TODO Pre processing here
for constraint in self.fit_constraints():
constraint()
return_data = func(x)
# TODO Loading or manipulating data here
return return_data

_fit_function.__signature__ = self._create_signature(self._cached_pars)
return _fit_function

@staticmethod
def _create_signature(parameters: Dict[int, Parameter]) -> Signature:
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@andped10 andped10 Aug 6, 2024 •
edited
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fetched from bumps / lmfit

"""
Wrap the function signature.
This is done as lmfit wants the function to be in the form:
f = (x, a=1, b=2)...
Where we need to be generic. Note that this won't hold for much outside of this scope.
"""
wrapped_parameters = []
wrapped_parameters.append(InspectParameter('x', InspectParameter.POSITIONAL_OR_KEYWORD, annotation=_empty))

## TODO clean when full move to new_variable
from easyscience.Objects.new_variable import Parameter as NewParameter

for name, parameter in parameters.items():
## TODO clean when full move to new_variable
if isinstance(parameter, NewParameter):
default_value = parameter.value
else:
default_value = parameter.raw_value

wrapped_parameters.append(
InspectParameter(
MINIMIZER_PARAMETER_PREFIX + str(name),
InspectParameter.POSITIONAL_OR_KEYWORD,
annotation=_empty,
default=default_value,
)
)
return Signature(wrapped_parameters)

@staticmethod
def _error_from_jacobian(jacobian: np.ndarray, residuals: np.ndarray, confidence: float = 0.95) -> np.ndarray:
from scipy import stats
Expand Down
86 changes: 0 additions & 86 deletions src/easyscience/fitting/minimizers/minimizer_bumps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
# © 2021-2023 Contributors to the EasyScience project <https://github.com/easyScience/EasyScience

import copy
import inspect
from typing import Callable
from typing import List
from typing import Optional
Expand Down Expand Up @@ -54,7 +53,6 @@ def __init__(
:type fit_function: Callable
"""
super().__init__(obj=obj, fit_function=fit_function, method=method)
self._cached_pars_order = ()
self._p_0 = {}

def available_methods(self) -> List[str]:
Expand Down Expand Up @@ -204,90 +202,6 @@ def _make_func(x, y, weights):

return _outer(self)

def _generate_fit_function(self) -> Callable:
"""
Using the user supplied `fit_function`, wrap it in such a way we can update `Parameter` on
iterations.

:return: a fit function which is compatible with bumps models
:rtype: Callable
"""
# Original fit function
func = self._original_fit_function
# Get a list of `Parameters`
self._cached_pars_vals = {}
for parameter in self._object.get_fit_parameters():
key = parameter.unique_name
self._cached_pars[key] = parameter
self._cached_pars_vals[key] = (parameter.value, parameter.error)

# Make a new fit function
def _fit_function(x: np.ndarray, **kwargs):
"""
Wrapped fit function which now has a bumps compatible form

:param x: array of data points to be calculated
:type x: np.ndarray
:param kwargs: key word arguments
:return: points calculated at `x`
:rtype: np.ndarray
"""
# Update the `Parameter` values and the callback if needed
## TODO clean when full move to new_variable
from easyscience.Objects.new_variable import Parameter

for name, value in kwargs.items():
par_name = name[1:]
if par_name in self._cached_pars.keys():
## TODO clean when full move to new_variable
if isinstance(self._cached_pars[par_name], Parameter):
if self._cached_pars[par_name].value != value:
self._cached_pars[par_name].value = value
else:
if self._cached_pars[par_name].raw_value != value:
self._cached_pars[par_name].value = value

# update_fun = self._cached_pars[par_name]._callback.fset
# if update_fun:
# update_fun(value)
# TODO Pre processing here
for constraint in self.fit_constraints():
constraint()
return_data = func(x)
# TODO Loading or manipulating data here
return return_data

# Fake the function signature.
# This is done as lmfit wants the function to be in the form:
# f = (x, a=1, b=2)...
# Where we need to be generic. Note that this won't hold for much outside of this scope.

## TODO clean when full move to new_variable
from easyscience.Objects.new_variable import Parameter

if isinstance(parameter, Parameter):
default_value = parameter.value
else:
default_value = parameter.raw_value

self._cached_pars_order = tuple(self._cached_pars.keys())
params = [
inspect.Parameter('x', inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=inspect._empty),
*[
inspect.Parameter(
MINIMIZER_PARAMETER_PREFIX + str(name),
inspect.Parameter.POSITIONAL_OR_KEYWORD,
annotation=inspect._empty,
default=default_value,
)
for name in self._cached_pars_order
],
]
# Sign the function
_fit_function.__signature__ = inspect.Signature(params)
self._fit_function = _fit_function
return _fit_function

def _set_parameter_fit_result(self, fit_result, stack_status: bool):
"""
Update parameters to their final values and assign a std error to them.
Expand Down
58 changes: 0 additions & 58 deletions src/easyscience/fitting/minimizers/minimizer_dfo.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -174,64 +174,6 @@ def _residuals(pars_values: List[float]) -> np.ndarray:

return _outer(self)

def _generate_fit_function(self) -> Callable:
"""
Using the user supplied `fit_function`, wrap it in such a way we can update `Parameter` on
iterations.

:return: a fit function which is compatible with bumps models
:rtype: Callable
"""
# Original fit function
func = self._original_fit_function
# Get a list of `Parameters`
self._cached_pars = {}
self._cached_pars_vals = {}
for parameter in self._object.get_fit_parameters():
key = parameter.unique_name
self._cached_pars[key] = parameter
self._cached_pars_vals[key] = (parameter.value, parameter.error)

# Make a new fit function
def _fit_function(x: np.ndarray, **kwargs):
"""
Wrapped fit function which now has an EasyScience compatible form

:param x: array of data points to be calculated
:type x: np.ndarray
:param kwargs: key word arguments
:return: points calculated at `x`
:rtype: np.ndarray
"""
# Update the `Parameter` values and the callback if needed
# TODO THIS IS NOT THREAD SAFE :-(
# TODO clean when full move to new_variable
from easyscience.Objects.new_variable import Parameter

for name, value in kwargs.items():
par_name = name[1:]
if par_name in self._cached_pars.keys():
# TODO clean when full move to new_variable
if isinstance(self._cached_pars[par_name], Parameter):
# This will take into account constraints
if self._cached_pars[par_name].value != value:
self._cached_pars[par_name].value = value
else:
# This will take into account constraints
if self._cached_pars[par_name].raw_value != value:
self._cached_pars[par_name].value = value

# Since we are calling the parameter fset will be called.
# TODO Pre processing here
for constraint in self.fit_constraints():
constraint()
return_data = func(x)
# TODO Loading or manipulating data here
return return_data

self._fit_function = _fit_function
return _fit_function

def _set_parameter_fit_result(self, fit_result, stack_status, ci: float = 0.95) -> None:
"""
Update parameters to their final values and assign a std error to them.
Expand Down
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