Restructure optimisers to enable passing of kwargs (#255)

* Enable passing of kwargs to SciPy * Update checks on bounds * Add update_options and error for Pints * Rename opt to optim * Remove stray comments * Add BaseSciPyOptimiser and BasePintsOptimiser * Align optimiser option setting * Update notebooks with kwargs * Update scripts with kwargs * Update notebooks * Align optimisers with Optimisation as base class * Update stopping criteria in spm_NelderMead.py Co-authored-by: Brady Planden <[email protected]> * Update stopping criteria in spm_adam.py Co-authored-by: Brady Planden <[email protected]> * Update sigma0 in spm_descent.py Co-authored-by: Brady Planden <[email protected]> * Update GradientDescent * Change update to set and check pints_method * Update test_optimisation_options * Update notebooks * Update set learning rate * Pop threshold * Fix bug in model.simulate * Update notebooks * Update test_models.py * Store SciPy result * Update x0 input and add tests * Update bounds to avoid x0 outside * Re-initialise pints_method on certain options * Update x0_new test * Update test_optimisation.py * Create initialise_method for PINTS optimisers * Align optimisation result * Update checks on bounds * Apply suggestions Co-authored-by: Brady Planden <[email protected]> * Add standalone optimiser * Simplify optimiser set-up and align _minimising * Update option setting in notebooks * Take abs of cost0 * Implement suggestions from Brady * Update tests and base option setting * Update test_invalid_cost * Increase coverage * Sort out notebook changes * Reset scale parameter * Move settings into arguments * Update comments * Update optimiser call * Move check on jac * Add assertions * Add maxiter to test * Add assertion * Update to lambda functions Co-authored-by: Brady Planden <[email protected]> * Update comment Co-authored-by: Brady Planden <[email protected]> * Update to list comprehension Co-authored-by: Brady Planden <[email protected]> * Formatting * Revert "Update to lambda functions" This reverts commit aa73bff. * Move minimising out of costs * Update description * Updates to #236 to avoid breaking change to `pybop.Optimisation` (#309) * Splits Optimisation -> BaseOptimiser/Optimisation, enables two optimisation APIs, updts where required, moves _optimisation to optimisers/ * increase coverage * Pass optimiser_kwargs though run() * updt examples * Converts DefaultOptimiser -> Optimisation * split Optimisation and BaseOptimsier classes, loosen standalone cost unit test * add incorrect attr test * fix: updt changelog entry, optimsation_interface notebook, review suggestions * fix: updt notebook state * Updt assertions, optimisation object name --------- Co-authored-by: NicolaCourtier <[email protected]> * Rename method to pints_optimiser * Rename base_optimiser to pints_base_optimiser * Rename _optimisation to base_optimiser --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Brady Planden <[email protected]>
pybop-team · Jun 3, 2024 · d6ddfbe · d6ddfbe
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -3,6 +3,7 @@
 ## Features
 
 - [#6](https://github.com/pybop-team/PyBOP/issues/6) - Adds Monte Carlo functionality, with methods based on Pints' algorithms. A base class is added `BaseSampler`, in addition to `PintsBaseSampler`.
+- [#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.

diff --git a/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb b/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)"

diff --git a/examples/notebooks/equivalent_circuit_identification.ipynb b/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)"

diff --git a/examples/notebooks/multi_model_identification.ipynb b/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)"

diff --git a/examples/notebooks/multi_optimiser_identification.ipynb b/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())"
    ]
   },
   {

diff --git a/examples/notebooks/optimiser_calibration.ipynb b/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\");"
    ]