Adds maximum iterations functionality to scipyminimize, nloptoptimize…

…, baseoptimise, restore test_spm_optimisers assertions

CHANGELOG.md

@@ -2,7 +2,7 @@
 
 ## Features
 
-- [#131](https://github.com/pybop-team/PyBOP/issues/131) - Adds `SciPyDifferentialEvolution` optimiser
+- [#131](https://github.com/pybop-team/PyBOP/issues/131) - Adds `SciPyDifferentialEvolution` optimiser, adds functionality for user-selectable maximum iteration limit to `SciPyMinimize`, `NLoptOptimize`, and `BaseOptimiser` classes.
 - [#107](https://github.com/pybop-team/PyBOP/issues/107) - Adds Equivalent Circuit Model (ECM) with examples, Import/Export parameter methods `ParameterSet.import_parameter` and `ParameterSet.export_parameters`, updates default FittingProblem.signal definition to `"Voltage [V]"`, and testing infrastructure
 - [#127](https://github.com/pybop-team/PyBOP/issues/127) - Adds Windows and macOS runners to the `test_on_push` action
 - [#114](https://github.com/pybop-team/PyBOP/issues/114) - Adds standard plotting class `pybop.StandardPlot()` via plotly backend

pybop/optimisation.py

@@ -135,6 +135,7 @@ def _run_pybop(self):
             cost_function=self.cost,
             x0=self.x0,
             bounds=self.bounds,
+            maxiter=self._max_iterations,
         )
         self.log = self.optimiser.log
 

pybop/optimisers/base_optimiser.py

@@ -8,14 +8,15 @@ class BaseOptimiser:
     def __init__(self):
         pass
 
-    def optimise(self, cost_function, x0=None, bounds=None):
+    def optimise(self, cost_function, x0=None, bounds=None, maxiter=None):
         """
         Optimisiation method to be overloaded by child classes.
 
         """
         self.cost_function = cost_function
         self.x0 = x0
         self.bounds = bounds
+        self.maxiter = maxiter
 
         # Run optimisation
         result = self._runoptimise(self.cost_function, x0=self.x0, bounds=self.bounds)

pybop/optimisers/nlopt_optimize.py

@@ -8,9 +8,10 @@ class NLoptOptimize(BaseOptimiser):
     Wrapper class for the NLOpt optimiser class. Extends the BaseOptimiser class.
     """
 
-    def __init__(self, n_param, xtol=None, method=None):
+    def __init__(self, n_param, xtol=None, method=None, maxiter=None):
         super().__init__()
         self.n_param = n_param
+        self.maxiter = maxiter
 
         if method is not None:
             self.optim = nlopt.opt(method, self.n_param)
@@ -46,6 +47,10 @@ def cost_wrapper(x, grad):
         self.optim.set_lower_bounds(bounds["lower"])
         self.optim.set_upper_bounds(bounds["upper"])
 
+        # Set max iterations
+        if self.maxiter is not None:
+            self.optim.set_maxeval(self.maxiter)
+
         # Run the optimser
         x = self.optim.optimize(x0)
 

pybop/optimisers/scipy_optimisers.py

@@ -7,10 +7,15 @@ class SciPyMinimize(BaseOptimiser):
     Wrapper class for the SciPy optimisation class. Extends the BaseOptimiser class.
     """
 
-    def __init__(self, method=None, bounds=None):
+    def __init__(self, method=None, bounds=None, maxiter=None):
         super().__init__()
         self.method = method
         self.bounds = bounds
+        self.maxiter = maxiter
+        if self.maxiter is not None:
+            self.options = {"maxiter": self.maxiter}
+        else:
+            self.options = {}
 
         if self.method is None:
             self.method = "COBYLA"  # "L-BFGS-B"
@@ -40,7 +45,12 @@ def callback(x):
             )
 
         output = minimize(
-            cost_function, x0, method=self.method, bounds=bounds, callback=callback
+            cost_function,
+            x0,
+            method=self.method,
+            bounds=bounds,
+            options=self.options,
+            callback=callback,
         )
 
         # Get performance statistics

tests/unit/test_parameterisations.py

@@ -138,13 +138,8 @@ def test_spm_optimisers(self, spm_cost, x0):
                 x, final_cost = parameterisation.run()
 
             # Assertions
-            # Note: SciPyMinimize has a different tolerance due to the local optimisation algorithms
-            if optimiser in [pybop.SciPyMinimize]:
-                np.testing.assert_allclose(final_cost, 0, atol=1e-2)
-                np.testing.assert_allclose(x, x0, atol=2e-1)
-            else:
-                np.testing.assert_allclose(final_cost, 0, atol=1e-2)
-                np.testing.assert_allclose(x, x0, atol=1e-1)
+            np.testing.assert_allclose(final_cost, 0, atol=1e-2)
+            np.testing.assert_allclose(x, x0, atol=1e-1)
 
     @pytest.mark.parametrize("init_soc", [0.3, 0.7])
     @pytest.mark.unit