Add support lmfit scipy least square

src/easyscience/fitting/available_minimizers.py

@@ -39,6 +39,7 @@ class AvailableMinimizers(Enum):
         LMFit_powell = auto()
         LMFit_cobyla = auto()
         LMFit_differential_evolution = auto()
+        LMFit_scipy_least_squares = auto()
 
     if bumps_engine_available:
         Bumps = auto()
@@ -63,6 +64,8 @@ def from_string_to_enum(minimizer_name: str) -> AvailableMinimizers:
         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

src/easyscience/fitting/minimizers/factory.py

@@ -23,6 +23,8 @@ def factory(minimizer_enum: AvailableMinimizers, fit_object, fit_function: Calla
         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.LMFit_scipy_least_squares:
+        minimizer = LMFit(obj=fit_object, fit_function=fit_function, method='least_squares')
 
     elif minimizer_enum == AvailableMinimizers.Bumps:
         minimizer = Bumps(obj=fit_object, fit_function=fit_function, method='amoeba')

tests/unit_tests/Fitting/minimizers/test_factory.py

@@ -12,7 +12,7 @@ def pull_minminizer(self, minimizer: AvailableMinimizers) -> MinimizerBase:
         minimizer = factory(minimizer, mock_fit_object, mock_fit_function)
         return minimizer
 
-    @pytest.mark.parametrize('minimizer_method,minimizer_enum', [('leastsq', AvailableMinimizers.LMFit), ('leastsq', AvailableMinimizers.LMFit_leastsq), ('powell', AvailableMinimizers.LMFit_powell), ('cobyla', AvailableMinimizers.LMFit_cobyla)])
+    @pytest.mark.parametrize('minimizer_method,minimizer_enum', [('leastsq', AvailableMinimizers.LMFit), ('leastsq', AvailableMinimizers.LMFit_leastsq), ('powell', AvailableMinimizers.LMFit_powell), ('cobyla', AvailableMinimizers.LMFit_cobyla), ('differential_evolution', AvailableMinimizers.LMFit_differential_evolution), ('least_squares', AvailableMinimizers.LMFit_scipy_least_squares)])
     def test_factory_lm_fit(self, minimizer_method, minimizer_enum):
         minimizer = self.pull_minminizer(minimizer_enum)
         assert minimizer._method == minimizer_method
@@ -31,7 +31,7 @@ def test_factory_dfo_fit(self, minimizer_method, minimizer_enum):
         assert minimizer.wrapping == 'dfo'
 
 
-@pytest.mark.parametrize('minimizer_name,expected', [('LMFit', AvailableMinimizers.LMFit), ('LMFit_leastsq', AvailableMinimizers.LMFit_leastsq), ('LMFit_powell', AvailableMinimizers.LMFit_powell), ('LMFit_cobyla', AvailableMinimizers.LMFit_cobyla), ])
+@pytest.mark.parametrize('minimizer_name,expected', [('LMFit', AvailableMinimizers.LMFit), ('LMFit_leastsq', AvailableMinimizers.LMFit_leastsq), ('LMFit_powell', AvailableMinimizers.LMFit_powell), ('LMFit_cobyla', AvailableMinimizers.LMFit_cobyla), ('LMFit_differential_evolution', AvailableMinimizers.LMFit_differential_evolution), ('LMFit_scipy_least_squares', AvailableMinimizers.LMFit_scipy_least_squares) ])
 def test_from_string_to_enum_lmfit(minimizer_name, expected):
     assert from_string_to_enum(minimizer_name) == expected
 
@@ -52,10 +52,11 @@ def test_available_minimizers():
     assert AvailableMinimizers.LMFit_powell
     assert AvailableMinimizers.LMFit_cobyla
     assert AvailableMinimizers.LMFit_differential_evolution
+    assert AvailableMinimizers.LMFit_scipy_least_squares
     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) == 11
+    assert len(AvailableMinimizers) == 12

tests/unit_tests/Fitting/minimizers/test_minimizer_lmfit.py

@@ -3,7 +3,6 @@
 from unittest.mock import MagicMock
 
 import easyscience.fitting.minimizers.minimizer_lmfit
-from easyscience.fitting import AvailableMinimizers
 from easyscience.fitting.minimizers.minimizer_lmfit import LMFit
 from easyscience.Objects.new_variable import Parameter
 from lmfit import Parameter as LMParameter
@@ -16,7 +15,7 @@ def minimizer(self) -> LMFit:
         minimizer = LMFit(
             obj='obj',
             fit_function='fit_function', 
-            method='least_squares'
+            method='leastsq'
         )
         return minimizer
 
@@ -99,7 +98,7 @@ def test_fit(self, minimizer: LMFit) -> None:
 
         # Expect
         assert result == 'gen_fit_results'
-        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='least_squares')
+        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='leastsq')
         minimizer._make_model.assert_called_once_with()
         minimizer._set_parameter_fit_result.assert_called_once_with('fit', False)
         minimizer._gen_fit_results.assert_called_once_with('fit')
@@ -116,7 +115,7 @@ def test_fit_model(self, minimizer: LMFit) -> None:
         minimizer.fit(x=1.0, y=2.0, model=mock_model)
 
         # Expect
-        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='least_squares')
+        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='leastsq')
         minimizer._make_model.assert_not_called()
 
     def test_fit_method(self, minimizer: LMFit) -> None:
@@ -148,7 +147,7 @@ def test_fit_kwargs(self, minimizer: LMFit) -> None:
         minimizer.fit(x=1.0, y=2.0, minimizer_kwargs={'minimizer_key': 'minimizer_val'}, engine_kwargs={'engine_key': 'engine_val'})
 
         # Expect
-        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='least_squares', fit_kws={'minimizer_key': 'minimizer_val'}, engine_key='engine_val')
+        mock_model.fit.assert_called_once_with(2.0, x=1.0, weights=0.7071067811865475, method='leastsq', fit_kws={'minimizer_key': 'minimizer_val'}, engine_key='engine_val')
 
     def test_fit_exception(self, minimizer: LMFit) -> None:
         # When

tests/unit_tests/Fitting/test_fitter.py

@@ -166,7 +166,7 @@ def test_available_minimizers(self, fitter: Fitter):
 
         # Then Expect
         assert minimizers == [
-            'LMFit', 'LMFit_leastsq', 'LMFit_powell', 'LMFit_cobyla', 'LMFit_differential_evolution',
+            'LMFit', 'LMFit_leastsq', 'LMFit_powell', 'LMFit_cobyla', 'LMFit_differential_evolution', 'LMFit_scipy_least_squares',
             'Bumps', 'Bumps_simplex', 'Bumps_newton', 'Bumps_lm',
             'DFO', 'DFO_leastsq'
         ]