removed strings in minimizer indexing mechanism #347

docs/docs/tutorials/progress-callback.ipynb

@@ -47,9 +47,9 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "from easyscience import AvailableMinimizers\n",
     "from easyscience import EasyList\n",
     "from easyscience import Fitter\n",
+    "from easyscience import Minimizers\n",
     "from easyscience import ModelBase\n",
     "from easyscience import Parameter"
    ]
@@ -264,7 +264,7 @@
    },
    "outputs": [],
    "source": [
-    "def fit_with(backend: AvailableMinimizers):\n",
+    "def fit_with(backend: Minimizers):\n",
     "    model = make_initial_model()\n",
     "    fitter = Fitter(model, model)\n",
     "    fitter.switch_minimizer(backend)\n",
@@ -277,7 +277,7 @@
     "\n",
     "results = {}\n",
     "histories = {}\n",
-    "for backend in (AvailableMinimizers.LMFit, AvailableMinimizers.Bumps, AvailableMinimizers.DFO):\n",
+    "for backend in (Minimizers.LMFit, Minimizers.Bumps, Minimizers.DFO):\n",
     "    res, hist = fit_with(backend)\n",
     "    results[backend.name] = res\n",
     "    histories[backend.name] = hist\n",
@@ -370,7 +370,7 @@
     "else:\n",
     "    model = make_initial_model()\n",
     "    fitter = Fitter(model, model)\n",
-    "    fitter.switch_minimizer(AvailableMinimizers.LMFit)\n",
+    "    fitter.switch_minimizer(Minimizers.LMFit)\n",
     "    fitter.max_evaluations = 108\n",
     "\n",
     "    pbar = tqdm(total=fitter.max_evaluations, desc='LMFit', unit='eval')\n",

docs/docs/user-guide/external-calculators.md

@@ -291,7 +291,9 @@ It is polled during the optimizer loop and once more after it finishes,
 so it must have no side effects:
 
 ```python
-fitter.switch_minimizer('Bumps')
+from easyscience import Minimizers
+
+fitter.switch_minimizer(Minimizers.Bumps)
 result = fitter.fit(
     x=x, y=y, weights=np.ones_like(x), abort_test=lambda: stop_requested
 )

src/easyscience/__init__.py

@@ -11,20 +11,20 @@
 
 from .base_classes import EasyList  # noqa: E402
 from .base_classes import ModelBase  # noqa: E402
-from .fitting import AvailableMinimizers  # noqa: E402
 from .fitting import Fitter  # noqa: E402
+from .fitting.available_minimizers import Minimizers  # noqa: E402
 from .variable import DescriptorNumber  # noqa: E402
 from .variable import Parameter  # noqa: E402
 
 __version__ = version('easyscience')
 
 __all__ = [
-    '__version__',
-    'global_object',
-    'ModelBase',
-    'EasyList',
-    'AvailableMinimizers',
-    'Fitter',
-    'DescriptorNumber',
-    'Parameter',
+    __version__,
+    global_object,
+    ModelBase,
+    EasyList,
+    Minimizers,
+    Fitter,
+    DescriptorNumber,
+    Parameter,
 ]

src/easyscience/fitting/__init__.py

@@ -1,7 +1,6 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-from .available_minimizers import AvailableMinimizers
 from .fitter import Fitter
 from .minimizers.utils import FitResults
 from .sampler import Sampler

src/easyscience/fitting/available_minimizers.py

@@ -48,7 +48,7 @@ class AvailableMinimizer:
     enum_id: int
 
 
-class AvailableMinimizers(AvailableMinimizer, Enum):
+class Minimizers(AvailableMinimizer, Enum):
     if lmfit_engine_available:
         LMFit = 'lm', 'leastsq', 11
         LMFit_leastsq = 'lm', 'leastsq', 12
@@ -66,39 +66,3 @@ class AvailableMinimizers(AvailableMinimizer, Enum):
     if dfo_engine_available:
         DFO = 'dfo', 'leastsq', 31
         DFO_leastsq = 'dfo', 'leastsq', 32
-
-
-# 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 == 'LMFit_scipy_least_squares':
-        minmizer_enum = AvailableMinimizers.LMFit_scipy_least_squares
-
-    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

@@ -4,21 +4,17 @@
 import functools
 from typing import Callable
 from typing import List
-from typing import Union
 
 import numpy as np
 
-from easyscience import global_object
-
-from .available_minimizers import AvailableMinimizers
-from .available_minimizers import from_string_to_enum
+from .available_minimizers import Minimizers
 from .minimizers import FitResults
 from .minimizers import MinimizerBase
 from .minimizers.factory import factory
 from .reshaping import inject_x
 from .reshaping import reshape_dataset
 
-DEFAULT_MINIMIZER = AvailableMinimizers.LMFit_leastsq
+DEFAULT_MINIMIZER = Minimizers.LMFit_leastsq
 
 
 class Fitter:
@@ -35,7 +31,7 @@ def __init__(self, fit_object, fit_function: Callable):
         self._max_evaluations: int = None
 
         self._minimizer: MinimizerBase = None  # set in _update_minimizer
-        self._enum_current_minimizer: AvailableMinimizers = None  # set in _update_minimizer
+        self._enum_current_minimizer: Minimizers = None  # set in _update_minimizer
         self._update_minimizer(DEFAULT_MINIMIZER)
 
     def make_model(self, pars=None) -> Callable:
@@ -60,42 +56,29 @@ def initialize(self, fit_object: object, fit_function: Callable) -> None:
         self._fit_function = fit_function
         self._update_minimizer(DEFAULT_MINIMIZER)
 
-    # TODO: remove this method when we are ready to adjust the dependent products
-    def create(self, minimizer_enum: Union[AvailableMinimizers, str] = DEFAULT_MINIMIZER) -> None:
-        """
-        Create the required minimizer.
-
-        Parameters
-        ----------
-        minimizer_enum : Union[AvailableMinimizers, str], default=DEFAULT_MINIMIZER
-            The enum of the minimization engine to create. By default,
-            DEFAULT_MINIMIZER.
-        """
-        if isinstance(minimizer_enum, str):
-            global_object.log.getLogger('fitting').warning(
-                'minimizer should be set with enum %s', minimizer_enum
-            )
-            minimizer_enum = from_string_to_enum(minimizer_enum)
-        self._update_minimizer(minimizer_enum)
-
-    def switch_minimizer(self, minimizer_enum: Union[AvailableMinimizers, str]) -> None:
+    def switch_minimizer(self, minimizer_enum: Minimizers) -> None:
         """
         Switch minimizer and initialize.
 
         Parameters
         ----------
-        minimizer_enum : Union[AvailableMinimizers, str]
+        minimizer_enum : Minimizers
             The enum of the minimizer to create and instantiate.
+
+        Raises
+        ------
+        TypeError
+            If ``minimizer_enum`` is not a ``Minimizers``
+            member.
         """
-        if isinstance(minimizer_enum, str):
-            global_object.log.getLogger('fitting').warning(
-                'minimizer should be set with enum %s', minimizer_enum
+        if not isinstance(minimizer_enum, Minimizers):
+            raise TypeError(
+                f'minimizer must be a Minimizers member, got {minimizer_enum!r}; '
+                f'use Minimizers[name] to convert a name'
             )
-            minimizer_enum = from_string_to_enum(minimizer_enum)
-
         self._update_minimizer(minimizer_enum)
 
-    def _update_minimizer(self, minimizer_enum: AvailableMinimizers) -> None:
+    def _update_minimizer(self, minimizer_enum: Minimizers) -> None:
         self._minimizer = factory(
             minimizer_enum=minimizer_enum,
             fit_object=self._fit_object,
@@ -104,16 +87,16 @@ def _update_minimizer(self, minimizer_enum: AvailableMinimizers) -> None:
         self._enum_current_minimizer = minimizer_enum
 
     @property
-    def available_minimizers(self) -> List[str]:
+    def available_minimizers(self) -> List[Minimizers]:
         """
-        Get a list of the names of available fitting minimizers.
+        Get a list of the available fitting minimizers.
 
         Returns
         -------
-        List[str]
+        List[Minimizers]
             List of available fitting minimizers.
         """
-        return [minimize.name for minimize in AvailableMinimizers]
+        return list(Minimizers)
 
     @property
     def minimizer(self) -> MinimizerBase:

src/easyscience/fitting/minimizers/factory.py

@@ -4,7 +4,7 @@
 from typing import Callable
 
 from .. import available_minimizers
-from ..available_minimizers import AvailableMinimizers
+from ..available_minimizers import Minimizers
 from .minimizer_base import MinimizerBase
 
 if available_minimizers.lmfit_engine_available:
@@ -15,9 +15,7 @@
     from .minimizer_bumps import Bumps
 
 
-def factory(
-    minimizer_enum: AvailableMinimizers, fit_object, fit_function: Callable
-) -> MinimizerBase:
+def factory(minimizer_enum: Minimizers, fit_object, fit_function: Callable) -> MinimizerBase:
     if minimizer_enum.package == 'lm':
         minimizer = LMFit(obj=fit_object, fit_function=fit_function, minimizer_enum=minimizer_enum)
 

src/easyscience/fitting/minimizers/minimizer_base.py

@@ -10,7 +10,7 @@
 
 from easyscience.variable import Parameter
 
-from ..available_minimizers import AvailableMinimizers
+from ..available_minimizers import Minimizers
 from ..engine_base import EngineBase
 from .utils import FitError
 from .utils import FitResults
@@ -26,7 +26,7 @@ def __init__(
         self,
         obj,  #: ModelBase,
         fit_function: Callable,
-        minimizer_enum: AvailableMinimizers,
+        minimizer_enum: Minimizers,
     ):  # todo after constraint changes, add type hint: obj: ModelBase  # noqa: E501
         if minimizer_enum.method not in self.supported_methods():
             raise FitError(f'Method {minimizer_enum.method} not available in {self.__class__}')
@@ -36,7 +36,7 @@ def __init__(
         self._cached_model = None
 
     @property
-    def enum(self) -> AvailableMinimizers:
+    def enum(self) -> Minimizers:
         return self._minimizer_enum
 
     @property

src/easyscience/fitting/minimizers/minimizer_bumps.py

@@ -17,7 +17,7 @@
 
 from easyscience.variable import Parameter
 
-from ..available_minimizers import AvailableMinimizers
+from ..available_minimizers import Minimizers
 from ..engine_base import PARAMETER_PREFIX
 from .bumps_utils import BumpsProgressMonitor
 from .bumps_utils import EvalCounter
@@ -49,7 +49,7 @@ def __init__(
         self,
         obj: object,
         fit_function: Callable,
-        minimizer_enum: AvailableMinimizers | None = None,
+        minimizer_enum: Minimizers | None = None,
     ):
         """
         Initialize the fitting engine.
@@ -60,7 +60,7 @@ def __init__(
             Object containing the ``Parameter`` instances to fit.
         fit_function : Callable
             Callable returning model y values for the supplied x values.
-        minimizer_enum : AvailableMinimizers | None, default=None
+        minimizer_enum : Minimizers | None, default=None
             Selected BUMPS minimizer configuration. By default, None.
         """
         super().__init__(obj=obj, fit_function=fit_function, minimizer_enum=minimizer_enum)

src/easyscience/fitting/minimizers/minimizer_dfo.py

@@ -12,7 +12,7 @@
 
 from easyscience.variable import Parameter
 
-from ..available_minimizers import AvailableMinimizers
+from ..available_minimizers import Minimizers
 from ..engine_base import PARAMETER_PREFIX
 from .minimizer_base import MinimizerBase
 from .utils import FitError
@@ -46,7 +46,7 @@ def __init__(
         self,
         obj: object,  #: ModelBase,
         fit_function: Callable,
-        minimizer_enum: AvailableMinimizers | None = None,
+        minimizer_enum: Minimizers | None = None,
     ):  # todo after constraint changes, add type hint: obj: ModelBase  # noqa: E501
         """
         Initialize the fitting engine.
@@ -57,7 +57,7 @@ def __init__(
             Object containing the ``Parameter`` instances to fit.
         fit_function : Callable
             Callable returning model y values for the supplied x values.
-        minimizer_enum : AvailableMinimizers | None, default=None
+        minimizer_enum : Minimizers | None, default=None
             Selected DFO minimizer configuration. By default, None.
         """
         super().__init__(obj=obj, fit_function=fit_function, minimizer_enum=minimizer_enum)

src/easyscience/fitting/minimizers/minimizer_lmfit.py

@@ -13,7 +13,7 @@
 
 from easyscience.variable import Parameter
 
-from ..available_minimizers import AvailableMinimizers
+from ..available_minimizers import Minimizers
 from ..engine_base import PARAMETER_PREFIX
 from .minimizer_base import MinimizerBase
 from .utils import FitError
@@ -34,7 +34,7 @@ def __init__(
         self,
         obj: object,  #: ModelBase,
         fit_function: Callable,
-        minimizer_enum: AvailableMinimizers | None = None,
+        minimizer_enum: Minimizers | None = None,
     ):  # todo after constraint changes, add type hint: obj: ModelBase  # noqa: E501
         """
         Initialize the minimizer.
@@ -45,7 +45,7 @@ def __init__(
             Object containing the ``Parameter`` instances to fit.
         fit_function : Callable
             Callable returning model y values for the supplied x values.
-        minimizer_enum : AvailableMinimizers | None, default=None
+        minimizer_enum : Minimizers | None, default=None
             Selected LMFit minimizer configuration. By default, None.
         """
         super().__init__(obj=obj, fit_function=fit_function, minimizer_enum=minimizer_enum)