60 add support lmfit differential evolution

src/easyscience/fitting/__init__.py

@@ -1,7 +1,8 @@
+from .available_minimizers import AvailableMinimizers
 from .fitter import Fitter
 from .minimizers.utils import FitResults
 
 # Causes circular import
 # from .multi_fitter import MultiFitter  # noqa: F401, E402
 
-all = [Fitter, FitResults]
+all = [AvailableMinimizers, Fitter, FitResults]

src/easyscience/fitting/available_minimizers.py

@@ -0,0 +1,85 @@
+import warnings
+from enum import Enum
+from enum import auto
+
+import pkg_resources
+
+installed_packages = {pkg.key for pkg in pkg_resources.working_set}
+
+# Change to importlib.metadata when Python 3.10 is the minimum version
+# import importlib.metadata
+# installed_packages = [x.name for x in importlib.metadata.distributions()]
+
+lmfit_engine_available = False
+if 'lmfit' in installed_packages:
+    lmfit_engine_available = True
+else:
+    # TODO make this a proper message (use logging?)
+    warnings.warn('LMFit minimization is not available. Probably lmfit has not been installed.', ImportWarning, stacklevel=2)
+
+bumps_engine_available = False
+if 'bumps' in installed_packages:
+    bumps_engine_available = True
+else:
+    # TODO make this a proper message (use logging?)
+    warnings.warn('Bumps minimization is not available. Probably bumps has not been installed.', ImportWarning, stacklevel=2)
+
+dfo_engine_available = False
+if 'dfo-ls' in installed_packages:
+    dfo_engine_available = True
+else:
+    # TODO make this a proper message (use logging?)
+    warnings.warn('DFO minimization is not available. Probably dfols has not been installed.', ImportWarning, stacklevel=2)
+
+
+class AvailableMinimizers(Enum):
+    if lmfit_engine_available:
+        LMFit = auto()
+        LMFit_leastsq = auto()
+        LMFit_powell = auto()
+        LMFit_cobyla = auto()
+        LMFit_differential_evolution = auto()
+
+    if bumps_engine_available:
+        Bumps = auto()
+        Bumps_simplex = auto()
+        Bumps_newton = auto()
+        Bumps_lm = auto()
+
+    if dfo_engine_available:
+        DFO = auto()
+        DFO_leastsq = auto()
+
+
+# Temporary solution to convert string to enum
+def from_string_to_enum(minimizer_name: str) -> AvailableMinimizers:
+    if minimizer_name == 'LMFit':
+        minmizer_enum = AvailableMinimizers.LMFit
+    elif minimizer_name == 'LMFit_leastsq':
+        minmizer_enum = AvailableMinimizers.LMFit_leastsq
+    elif minimizer_name == 'LMFit_powell':
+        minmizer_enum = AvailableMinimizers.LMFit_powell
+    elif minimizer_name == 'LMFit_cobyla':
+        minmizer_enum = AvailableMinimizers.LMFit_cobyla
+    elif minimizer_name == 'LMFit_differential_evolution':
+        minmizer_enum = AvailableMinimizers.LMFit_differential_evolution
+
+    elif minimizer_name == 'Bumps':
+        minmizer_enum = AvailableMinimizers.Bumps
+    elif minimizer_name == 'Bumps_simplex':
+        minmizer_enum = AvailableMinimizers.Bumps_simplex
+    elif minimizer_name == 'Bumps_newton':
+        minmizer_enum = AvailableMinimizers.Bumps_newton
+    elif minimizer_name == 'Bumps_lm':
+        minmizer_enum = AvailableMinimizers.Bumps_lm
+
+    elif minimizer_name == 'DFO':
+        minmizer_enum = AvailableMinimizers.DFO
+    elif minimizer_name == 'DFO_leastsq':
+        minmizer_enum = AvailableMinimizers.DFO_leastsq
+    else:
+        raise ValueError(
+            f'Invalid minimizer name: {minimizer_name}. The following minimizers are available: {[minimize.name for minimize in AvailableMinimizers]}'  # noqa: E501
+        )
+
+    return minmizer_enum

src/easyscience/fitting/fitter.py

@@ -9,11 +9,11 @@
 
 import numpy as np
 
+from .available_minimizers import AvailableMinimizers
+from .available_minimizers import from_string_to_enum
 from .minimizers import FitResults
 from .minimizers import MinimizerBase
-from .minimizers.factory import AvailableMinimizers
 from .minimizers.factory import factory
-from .minimizers.factory import from_string_to_enum
 
 DEFAULT_MINIMIZER = AvailableMinimizers.LMFit_leastsq
 

src/easyscience/fitting/minimizers/factory.py

@@ -1,83 +1,15 @@
-import warnings
-from enum import Enum
-from enum import auto
 from typing import Callable
 
+from .. import available_minimizers
+from ..available_minimizers import AvailableMinimizers
 from .minimizer_base import MinimizerBase
 
-lmfit_engine_imported = False
-try:
+if available_minimizers.lmfit_engine_available:
     from .minimizer_lmfit import LMFit
-
-    lmfit_engine_imported = True
-except ImportError:
-    # TODO make this a proper message (use logging?)
-    warnings.warn('LMFit minimization is not available. Probably lmfit has not been installed.', ImportWarning, stacklevel=2)
-
-bumps_engine_imported = False
-try:
-    from .minimizer_bumps import Bumps
-
-    bumps_engine_imported = True
-except ImportError:
-    # TODO make this a proper message (use logging?)
-    warnings.warn('Bumps minimization is not available. Probably bumps has not been installed.', ImportWarning, stacklevel=2)
-
-dfo_engine_imported = False
-try:
+if available_minimizers.dfo_engine_available:
     from .minimizer_dfo import DFO
-
-    dfo_engine_imported = True
-except ImportError:
-    # TODO make this a proper message (use logging?)
-    warnings.warn('DFO minimization is not available. Probably dfols has not been installed.', ImportWarning, stacklevel=2)
-
-
-class AvailableMinimizers(Enum):
-    if lmfit_engine_imported:
-        LMFit = auto()
-        LMFit_leastsq = auto()
-        LMFit_powell = auto()
-        LMFit_cobyla = auto()
-
-    if bumps_engine_imported:
-        Bumps = auto()
-        Bumps_simplex = auto()
-        Bumps_newton = auto()
-        Bumps_lm = auto()
-
-    if dfo_engine_imported:
-        DFO = auto()
-        DFO_leastsq = auto()
-
-# Temporary solution to convert string to enum
-def from_string_to_enum(minimizer_name: str) -> AvailableMinimizers:
-    if minimizer_name == 'LMFit':
-        minmizer_enum = AvailableMinimizers.LMFit
-    elif minimizer_name == 'LMFit_leastsq':
-        minmizer_enum = AvailableMinimizers.LMFit_leastsq
-    elif minimizer_name == 'LMFit_powell':
-        minmizer_enum = AvailableMinimizers.LMFit_powell
-    elif minimizer_name == 'LMFit_cobyla':
-        minmizer_enum = AvailableMinimizers.LMFit_cobyla
-
-    elif minimizer_name == 'Bumps':
-        minmizer_enum = AvailableMinimizers.Bumps
-    elif minimizer_name == 'Bumps_simplex':
-        minmizer_enum = AvailableMinimizers.Bumps_simplex
-    elif minimizer_name == 'Bumps_newton':
-        minmizer_enum = AvailableMinimizers.Bumps_newton
-    elif minimizer_name == 'Bumps_lm':
-        minmizer_enum = AvailableMinimizers.Bumps_lm
-
-    elif minimizer_name == 'DFO':
-        minmizer_enum = AvailableMinimizers.DFO
-    elif minimizer_name == 'DFO_leastsq':
-        minmizer_enum = AvailableMinimizers.DFO_leastsq
-    else:
-        raise ValueError(f"Invalid minimizer name: {minimizer_name}. The following minimizers are available: {[minimize.name for minimize in AvailableMinimizers]}")          # noqa: E501
-
-    return minmizer_enum
+if available_minimizers.bumps_engine_available:
+    from .minimizer_bumps import Bumps
 
 
 def factory(minimizer_enum: AvailableMinimizers, fit_object, fit_function: Callable) -> MinimizerBase:
@@ -89,6 +21,8 @@ def factory(minimizer_enum: AvailableMinimizers, fit_object, fit_function: Calla
         minimizer = LMFit(obj=fit_object, fit_function=fit_function, method='powell')
     elif minimizer_enum == AvailableMinimizers.LMFit_cobyla:
         minimizer = LMFit(obj=fit_object, fit_function=fit_function, method='cobyla')
+    elif minimizer_enum == AvailableMinimizers.LMFit_differential_evolution:
+        minimizer = LMFit(obj=fit_object, fit_function=fit_function, method='differential_evolution')
 
     elif minimizer_enum == AvailableMinimizers.Bumps:
         minimizer = Bumps(obj=fit_object, fit_function=fit_function, method='amoeba')

src/easyscience/fitting/minimizers/minimizer_base.py

@@ -16,8 +16,8 @@
 
 import numpy as np
 
-#causes circular import when Parameter is imported
-#from easyscience.Objects.ObjectClasses import BaseObj 
+# causes circular import when Parameter is imported
+# from easyscience.Objects.ObjectClasses import BaseObj
 from easyscience.Objects.Variable import Parameter
 
 from ..Constraints import ObjConstraint
@@ -95,7 +95,7 @@ def fit(
         :return: Fit results
         """
 
-    def evaluate(self, x: np.ndarray, minimizer_parameters: dict[str, float] = None, **kwargs) -> np.ndarray:
+    def evaluate(self, x: np.ndarray, minimizer_parameters: Optional[dict[str, float]] = None, **kwargs) -> np.ndarray:
         """
         Evaluate the fit function for values of x. Parameters used are either the latest or user supplied.
         If the parameters are user supplied, it must be in a dictionary of {'parameter_name': parameter_value,...}.
@@ -122,6 +122,17 @@ def evaluate(self, x: np.ndarray, minimizer_parameters: dict[str, float] = None,
 
         return self._fit_function(x, **minimizer_parameters, **kwargs)
 
+    def _get_method_dict(self, passed_method: Optional[str] = None) -> dict[str, str]:
+        if passed_method is not None:
+            if passed_method not in self.supported_methods():
+                raise FitError(f'Method {passed_method} not available in {self.__class__}')
+            return {'method': passed_method}
+
+        if self._method is not None:
+            return {'method': self._method}
+
+        return {}
+
     @abstractmethod
     def convert_to_pars_obj(self, par_list: Optional[Union[list]] = None):
         """

src/easyscience/fitting/minimizers/minimizer_bumps.py

@@ -62,7 +62,7 @@ def all_methods() -> List[str]:
     @staticmethod
     def supported_methods() -> List[str]:
         # only a small subset
-        methods = ['scipy.leastsq','amoeba', 'newton', 'lm']
+        methods = ['scipy.leastsq', 'amoeba', 'newton', 'lm']
         return methods
 
     def fit(
@@ -96,11 +96,7 @@ def fit(
         :return: Fit results
         :rtype: ModelResult
         """
-        default_method = {}
-        if self._method is not None:
-            default_method = {'method': self._method}
-        if method is not None and method in self.supported_methods():
-            default_method['method'] = method
+        method_dict = self._get_method_dict(method)
 
         if weights is None:
             weights = np.sqrt(np.abs(y))
@@ -135,7 +131,7 @@ def fit(
         global_object.stack.enabled = False
 
         try:
-            model_results = bumps_fit(problem, **default_method, **minimizer_kwargs, **kwargs)
+            model_results = bumps_fit(problem, **method_dict, **minimizer_kwargs, **kwargs)
             self._set_parameter_fit_result(model_results, stack_status)
             results = self._gen_fit_results(model_results)
         except Exception as e:

src/easyscience/fitting/minimizers/minimizer_dfo.py

@@ -87,12 +87,6 @@ def fit(
         :return: Fit results
         :rtype: ModelResult
         """
-        default_method = {}
-        if self._method is not None:
-            default_method = {'method': self._method}
-        if method is not None and method in self.supported_methods():
-            default_method['method'] = method
-
         if weights is None:
             weights = np.sqrt(np.abs(y))
 

src/easyscience/fitting/minimizers/minimizer_lmfit.py

@@ -71,6 +71,7 @@ def supported_methods() -> List[str]:
         return [
             'least_squares',
             'leastsq',
+            'differential_evolution',
             'powell',
             'cobyla',
         ]
@@ -108,14 +109,7 @@ def fit(
         :return: Fit results
         :rtype: ModelResult
         """
-        default_method = {}
-        if self._method is not None:
-            default_method = {'method': self._method}
-        if method is not None:
-            if method in self.supported_methods():
-                default_method['method'] = method
-            else:
-                raise FitError(f'Method {method} not available in {self.__class__}')
+        method_dict = self._get_method_dict(method)
 
         if weights is None:
             weights = 1 / np.sqrt(np.abs(y))
@@ -139,7 +133,7 @@ def fit(
             if model is None:
                 model = self._make_model()
 
-            model_results = model.fit(y, x=x, weights=weights, **default_method, **minimizer_kwargs, **kwargs)
+            model_results = model.fit(y, x=x, weights=weights, **method_dict, **minimizer_kwargs, **kwargs)
             self._set_parameter_fit_result(model_results, stack_status)
             results = self._gen_fit_results(model_results)
         except Exception as e:

tests/integration_tests/Fitting/test_fitter.py

@@ -11,7 +11,7 @@
 from easyscience.fitting.Constraints import ObjConstraint
 from easyscience.fitting.fitter import Fitter
 from easyscience.fitting.minimizers import FitError
-from easyscience.fitting.minimizers.factory import AvailableMinimizers
+from easyscience.fitting.available_minimizers import AvailableMinimizers
 from easyscience.Objects.ObjectClasses import BaseObj
 from easyscience.Objects.new_variable import Parameter
 

tests/integration_tests/Fitting/test_fitter_legacy_parameter.py

@@ -11,7 +11,7 @@
 from easyscience.fitting.Constraints import ObjConstraint
 from easyscience.fitting.fitter import Fitter
 from easyscience.fitting.minimizers import FitError
-from easyscience.fitting.minimizers.factory import AvailableMinimizers
+from easyscience.fitting.available_minimizers import AvailableMinimizers
 from easyscience.Objects.ObjectClasses import BaseObj
 from easyscience.Objects.ObjectClasses import Parameter
 

tests/unit_tests/Fitting/minimizers/test_factory.py

@@ -1,6 +1,6 @@
 from easyscience.fitting.minimizers.factory import factory
-from easyscience.fitting.minimizers.factory import from_string_to_enum
-from easyscience.fitting.minimizers.factory import AvailableMinimizers
+from easyscience.fitting.available_minimizers import from_string_to_enum
+from easyscience.fitting.available_minimizers import AvailableMinimizers
 from easyscience.fitting.minimizers import MinimizerBase
 from unittest.mock import MagicMock
 import pytest
@@ -51,10 +51,11 @@ def test_available_minimizers():
     assert AvailableMinimizers.LMFit_leastsq
     assert AvailableMinimizers.LMFit_powell
     assert AvailableMinimizers.LMFit_cobyla
+    assert AvailableMinimizers.LMFit_differential_evolution
     assert AvailableMinimizers.Bumps
     assert AvailableMinimizers.Bumps_simplex
     assert AvailableMinimizers.Bumps_newton
     assert AvailableMinimizers.Bumps_lm
     assert AvailableMinimizers.DFO
     assert AvailableMinimizers.DFO_leastsq
-    assert len(AvailableMinimizers) == 10
+    assert len(AvailableMinimizers) == 11