Restructure optimisers to enable passing of kwargs

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes, adds a new optimisation API through direct construction and keyword arguments, and fixes the setting of `max_iterations`, and `_minimising`. Introduces `pybop.BaseOptimiser`, `pybop.BasePintsOptimiser`, and `pybop.BaseSciPyOptimiser` classes.
 - [#321](https://github.com/pybop-team/PyBOP/pull/321) - Updates Prior classes with BaseClass, adds a `problem.sample_initial_conditions` method to improve stability of SciPy.Minimize optimiser.
 - [#249](https://github.com/pybop-team/PyBOP/pull/249) - Add WeppnerHuggins model and GITT example.
 - [#304](https://github.com/pybop-team/PyBOP/pull/304) - Decreases the testing suite completion time.

examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb

@@ -1639,8 +1639,7 @@
     }
    ],
    "source": [
-    "optim = pybop.Optimisation(cost, optimiser=pybop.PSO)\n",
-    "optim.set_max_unchanged_iterations(iterations=55, threshold=1e-6)\n",
+    "optim = pybop.PSO(cost, max_unchanged_iterations=55, threshold=1e-6)\n",
     "x, final_cost = optim.run()\n",
     "print(\"Initial parameters:\", cost.x0)\n",
     "print(\"Estimated parameters:\", x)"

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -417,8 +417,7 @@
     }
    ],
    "source": [
-    "optim = pybop.Optimisation(cost, optimiser=pybop.CMAES)\n",
-    "optim.set_max_iterations(300)\n",
+    "optim = pybop.CMAES(cost, max_iterations=300)\n",
     "x, final_cost = optim.run()\n",
     "print(\"Initial parameters:\", cost.x0)\n",
     "print(\"Estimated parameters:\", x)"

examples/notebooks/multi_model_identification.ipynb

@@ -3813,9 +3813,9 @@
     "    print(f\"Running {model.name}\")\n",
     "    problem = pybop.FittingProblem(model, parameters, dataset, init_soc=init_soc)\n",
     "    cost = pybop.SumSquaredError(problem)\n",
-    "    optim = pybop.Optimisation(cost, optimiser=pybop.XNES, verbose=True)\n",
-    "    optim.set_max_iterations(60)\n",
-    "    optim.set_max_unchanged_iterations(15)\n",
+    "    optim = pybop.XNES(\n",
+    "        cost, verbose=True, max_iterations=60, max_unchanged_iterations=15\n",
+    "    )\n",
     "    x, final_cost = optim.run()\n",
     "    optims.append(optim)\n",
     "    xs.append(x)"

examples/notebooks/multi_optimiser_identification.ipynb

@@ -358,9 +358,7 @@
     "cost = pybop.SumSquaredError(problem)\n",
     "for optimiser in gradient_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",
-    "    optim = pybop.Optimisation(cost, optimiser=optimiser)\n",
-    "    optim.set_max_unchanged_iterations(20)\n",
-    "    optim.set_max_iterations(60)\n",
+    "    optim = optimiser(cost, max_unchanged_iterations=20, max_iterations=60)\n",
     "    x, _ = optim.run()\n",
     "    optims.append(optim)\n",
     "    xs.append(x)"
@@ -387,9 +385,7 @@
    "source": [
     "for optimiser in non_gradient_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",
-    "    optim = pybop.Optimisation(cost, optimiser=optimiser)\n",
-    "    optim.set_max_unchanged_iterations(20)\n",
-    "    optim.set_max_iterations(60)\n",
+    "    optim = optimiser(cost, max_unchanged_iterations=20, max_iterations=60)\n",
     "    x, _ = optim.run()\n",
     "    optims.append(optim)\n",
     "    xs.append(x)"
@@ -413,9 +409,7 @@
    "source": [
     "for optimiser in scipy_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",
-    "    optim = pybop.Optimisation(cost, optimiser=optimiser)\n",
-    "    optim.set_max_unchanged_iterations(20)\n",
-    "    optim.set_max_iterations(60)\n",
+    "    optim = optimiser(cost, max_iterations=60)\n",
     "    x, _ = optim.run()\n",
     "    optims.append(optim)\n",
     "    xs.append(x)"
@@ -462,14 +456,7 @@
    ],
    "source": [
     "for optim in optims:\n",
-    "    if isinstance(\n",
-    "        optim.optimiser, (pybop.SciPyMinimize, pybop.SciPyDifferentialEvolution)\n",
-    "    ):\n",
-    "        print(f\"| Optimiser: {optim.optimiser.name()} | Results: {optim.result.x} |\")\n",
-    "    else:\n",
-    "        print(\n",
-    "            f\"| Optimiser: {optim.optimiser.name()} | Results: {optim.optimiser.x_best()} |\"\n",
-    "        )"
+    "    print(f\"| Optimiser: {optim.name()} | Results: {optim.result.x} |\")"
    ]
   },
   {
@@ -612,9 +599,7 @@
    ],
    "source": [
     "for optim, x in zip(optims, xs):\n",
-    "    pybop.quick_plot(\n",
-    "        optim.cost.problem, parameter_values=x, title=optim.optimiser.name()\n",
-    "    )"
+    "    pybop.quick_plot(optim.cost.problem, parameter_values=x, title=optim.name())"
    ]
   },
   {
@@ -822,7 +807,7 @@
    ],
    "source": [
     "for optim in optims:\n",
-    "    pybop.plot_convergence(optim, title=optim.optimiser.name())\n",
+    "    pybop.plot_convergence(optim, title=optim.name())\n",
     "    pybop.plot_parameters(optim)"
    ]
   },
@@ -942,7 +927,7 @@
     "# Plot the cost landscape with optimisation path and updated bounds\n",
     "bounds = np.array([[0.5, 0.8], [0.55, 0.8]])\n",
     "for optim in optims:\n",
-    "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.optimiser.name())"
+    "    pybop.plot2d(optim, bounds=bounds, steps=10, title=optim.name())"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -281,8 +281,7 @@
    "source": [
     "problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "cost = pybop.SumSquaredError(problem)\n",
-    "optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent, sigma0=0.2)\n",
-    "optim.set_max_iterations(100)"
+    "optim = pybop.GradientDescent(cost, sigma0=0.2, max_iterations=100)"
    ]
   },
   {
@@ -456,8 +455,7 @@
     "    print(sigma)\n",
     "    problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "    cost = pybop.SumSquaredError(problem)\n",
-    "    optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent, sigma0=sigma)\n",
-    "    optim.set_max_iterations(100)\n",
+    "    optim = pybop.GradientDescent(cost, sigma0=sigma, max_iterations=100)\n",
     "    x, final_cost = optim.run()\n",
     "    optims.append(optim)\n",
     "    xs.append(x)"
@@ -490,7 +488,7 @@
    "source": [
     "for optim, sigma in zip(optims, sigmas):\n",
     "    print(\n",
-    "        f\"| Sigma: {sigma} | Num Iterations: {optim._iterations} | Best Cost: {optim.optimiser.f_best()} | Results: {optim.optimiser.x_best()} |\"\n",
+    "        f\"| Sigma: {sigma} | Num Iterations: {optim._iterations} | Best Cost: {optim.pints_optimiser.f_best()} | Results: {optim.pints_optimiser.x_best()} |\"\n",
     "    )"
    ]
   },
@@ -723,7 +721,7 @@
     }
    ],
    "source": [
-    "optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent, sigma0=0.0115)\n",
+    "optim = pybop.GradientDescent(cost, sigma0=0.0115)\n",
     "x, final_cost = optim.run()\n",
     "pybop.quick_plot(problem, parameter_values=x, title=\"Optimised Comparison\");"
    ]