Updt SciPy & BaseOptimiser for maximum iterations limit - fixes #237

pybop/_optimisation.py

@@ -175,15 +175,16 @@ def _run_pybop(self):
         final_cost : float
             The final cost associated with the best parameters.
         """
-        x, final_cost = self.optimiser.optimise(
+        result = self.optimiser.optimise(
             cost_function=self.cost,
             x0=self.x0,
             bounds=self.bounds,
             maxiter=self._max_iterations,
         )
         self.log = self.optimiser.log
+        self._iterations = result.nit
 
-        return x, final_cost
+        return result.x, self.cost(result.x)
 
     def _run_pints(self):
         """

pybop/optimisers/base_optimiser.py

@@ -38,7 +38,7 @@ def optimise(self, cost_function, x0=None, bounds=None, maxiter=None):
         self.cost_function = cost_function
         self.x0 = x0
         self.bounds = bounds
-        self.maxiter = maxiter
+        self._max_iterations = maxiter
 
         # Run optimisation
         result = self._runoptimise(self.cost_function, x0=self.x0, bounds=self.bounds)

pybop/optimisers/scipy_optimisers.py

@@ -80,7 +80,7 @@ def cost_wrapper(x):
         else:
             self.options.pop("maxiter", None)
 
-        output = minimize(
+        result = minimize(
             cost_wrapper,
             x0,
             method=self.method,
@@ -89,11 +89,7 @@ def cost_wrapper(x):
             callback=callback,
         )
 
-        # Get performance statistics
-        x = output.x
-        final_cost = cost_function(x)
-
-        return x, final_cost
+        return result
 
     def needs_sensitivities(self):
         """
@@ -182,7 +178,7 @@ def callback(x, convergence):
                 (lower, upper) for lower, upper in zip(bounds["lower"], bounds["upper"])
             ]
 
-        output = differential_evolution(
+        result = differential_evolution(
             cost_function,
             bounds,
             strategy=self.strategy,
@@ -191,11 +187,7 @@ def callback(x, convergence):
             callback=callback,
         )
 
-        # Get performance statistics
-        x = output.x
-        final_cost = output.fun
-
-        return x, final_cost
+        return result
 
     def set_population_size(self, population_size=None):
         """