Refactors OptimisationResult class, adds finite cost check for identified parameters

CHANGELOG.md

@@ -14,6 +14,8 @@
 
 ## Breaking Changes
 
+- [#526](https://github.com/pybop-team/PyBOP/pull/526) - Refactor `OptimisationResults` classes, with `optim.run()` now return the full object. Adds finite cost value check for optimised parameters.
+
 # [v24.9.1](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-16
 
 ## Features

benchmarks/benchmark_track_parameterisation.py

@@ -123,5 +123,5 @@ def results_tracking(self, model, parameter_set, optimiser):
             parameter_set (str): The name of the parameter set being used (unused).
             optimiser (pybop.Optimiser): The optimizer class being used (unused).
         """
-        x, _ = self.optim.run()
-        return x
+        results = self.optim.run()
+        return results.x

examples/notebooks/adamw_identification.ipynb

@@ -301,7 +301,7 @@
    },
    "outputs": [],
    "source": [
-    "x, final_cost = optim.run()"
+    "results = optim.run()"
    ]
   },
   {
@@ -338,7 +338,7 @@
     }
    ],
    "source": [
-    "x  # This will output the estimated parameters"
+    "results.x  # This will output the estimated parameters"
    ]
   },
   {
@@ -434,7 +434,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/ecm_trust-constr.ipynb

@@ -403,14 +403,14 @@
     }
    ],
    "source": [
-    "x, final_cost = optim.run()\n",
-    "print(\"Estimated parameters:\", x)\n",
+    "results = optim.run()\n",
+    "print(\"Estimated parameters:\", results.x)\n",
     "\n",
     "# Plot the time series\n",
     "pybop.plot_dataset(dataset)\n",
     "\n",
     "# Plot the timeseries output\n",
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\")\n",
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\")\n",
     "\n",
     "# Plot convergence\n",
     "pybop.plot_convergence(optim)\n",

examples/notebooks/electrode_balancing.ipynb

@@ -246,9 +246,9 @@
    ],
    "source": [
     "optim = pybop.SciPyMinimize(cost, max_iterations=300)\n",
-    "x, final_cost = optim.run()\n",
+    "results = optim.run()\n",
     "print(\"Initial parameters:\", optim.x0)\n",
-    "print(\"Estimated parameters:\", x)"
+    "print(\"Estimated parameters:\", results.x)"
    ]
   },
   {
@@ -315,7 +315,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -387,8 +387,8 @@
     "    ),\n",
     ")\n",
     "\n",
-    "Q = x[1]\n",
-    "sto_0 = x[0]\n",
+    "Q = results.x[1]\n",
+    "sto_0 = results.x[0]\n",
     "\n",
     "sto = measured_data[\"Capacity [A.h]\"].to_numpy() / Q + sto_0\n",
     "\n",

examples/notebooks/energy_based_electrode_design.ipynb

@@ -245,10 +245,10 @@
     }
    ],
    "source": [
-    "x, final_cost = optim.run()\n",
-    "print(\"Estimated parameters:\", x)\n",
+    "results = optim.run()\n",
+    "print(\"Estimated parameters:\", results.x)\n",
     "print(f\"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1\")\n",
-    "print(f\"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1\")"
+    "print(f\"Optimised gravimetric energy density: {results.final_cost:.2f} Wh.kg-1\")"
    ]
   },
   {
@@ -339,7 +339,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification.ipynb

@@ -322,9 +322,9 @@
     "    max_unchanged_iterations=50,\n",
     "    max_iterations=250,\n",
     ")\n",
-    "x, final_cost = optim.run()\n",
+    "results = optim.run()\n",
     "print(\"Initial parameters:\", optim.x0)\n",
-    "print(\"Estimated parameters:\", x)"
+    "print(\"Estimated parameters:\", results.x)"
    ]
   },
   {
@@ -402,7 +402,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/equivalent_circuit_identification_hppc.ipynb

@@ -252,9 +252,9 @@
     "    max_unchanged_iterations=30,\n",
     "    max_iterations=100,\n",
     ")\n",
-    "x, final_cost = optim.run()\n",
+    "results = optim.run()\n",
     "print(\"Initial parameters:\", optim.x0)\n",
-    "print(\"Estimated parameters:\", x)"
+    "print(\"Estimated parameters:\", results.x)"
    ]
   },
   {
@@ -332,7 +332,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -508,7 +508,7 @@
    ],
    "source": [
     "sol = model.predict(\n",
-    "    inputs=x,\n",
+    "    inputs=results.x,\n",
     "    experiment=experiment,\n",
     "    parameter_set=parameter_set,\n",
     "    initial_state={\"Initial SoC\": 0.95},\n",

examples/notebooks/equivalent_circuit_identification_multipulse.ipynb

@@ -413,9 +413,9 @@
     "    max_unchanged_iterations=20,\n",
     "    max_iterations=100,\n",
     ")\n",
-    "x, final_cost = optim.run()\n",
+    "results = optim.run()\n",
     "print(\"Initial parameters:\", optim.x0)\n",
-    "print(\"Estimated parameters:\", x)"
+    "print(\"Estimated parameters:\", results.x)"
    ]
   },
   {
@@ -469,7 +469,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/maximum_a_posteriori.ipynb

@@ -451,8 +451,8 @@
     "    max_iterations=100,\n",
     ")\n",
     "start_time = time.time()\n",
-    "x, final_cost = optim.run()\n",
-    "print(f\"Inferred Parameters: {x} in {time.time() - start_time} seconds\")\n",
+    "results = optim.run()\n",
+    "print(f\"Inferred Parameters: {results.x} in {time.time() - start_time} seconds\")\n",
     "print(f\"True Parameters: {parameters.true_value()}\")\n",
     "pybop.plot_parameters(optim);"
    ]

examples/notebooks/multi_model_identification.ipynb

@@ -379,9 +379,9 @@
     "    optim = pybop.XNES(\n",
     "        cost, verbose=True, max_iterations=60, max_unchanged_iterations=15\n",
     "    )\n",
-    "    x, final_cost = optim.run()\n",
+    "    results = optim.run()\n",
     "    optims.append(optim)\n",
-    "    xs.append(x)"
+    "    xs.append(results.x)"
    ]
   },
   {

examples/notebooks/multi_optimiser_identification.ipynb

@@ -336,9 +336,9 @@
     "    optim = optimiser(\n",
     "        cost, sigma0=sigma0, max_unchanged_iterations=20, max_iterations=60\n",
     "    )\n",
-    "    x, _ = optim.run()\n",
+    "    results = optim.run()\n",
     "    optims.append(optim)\n",
-    "    xs.append(x)"
+    "    xs.append(results.x)"
    ]
   },
   {
@@ -394,9 +394,9 @@
     "for optimiser in non_gradient_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",
     "    optim = optimiser(cost, max_unchanged_iterations=20, max_iterations=60)\n",
-    "    x, _ = optim.run()\n",
+    "    results = optim.run()\n",
     "    optims.append(optim)\n",
-    "    xs.append(x)"
+    "    xs.append(results.x)"
    ]
   },
   {
@@ -424,9 +424,9 @@
     "for optimiser in scipy_optimisers:\n",
     "    print(f\"Running {optimiser.__name__}\")\n",
     "    optim = optimiser(cost, max_iterations=60)\n",
-    "    x, _ = optim.run()\n",
+    "    results = optim.run()\n",
     "    optims.append(optim)\n",
-    "    xs.append(x)"
+    "    xs.append(results.x)"
    ]
   },
   {

examples/notebooks/optimiser_calibration.ipynb

@@ -295,7 +295,7 @@
    },
    "outputs": [],
    "source": [
-    "x, final_cost = optim.run()"
+    "results = optim.run()"
    ]
   },
   {
@@ -332,7 +332,7 @@
     }
    ],
    "source": [
-    "x  # This will output the estimated parameters"
+    "results.x  # This will output the estimated parameters"
    ]
   },
   {
@@ -406,7 +406,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -497,9 +497,9 @@
     "    problem = pybop.FittingProblem(model, parameters, dataset)\n",
     "    cost = pybop.SumSquaredError(problem)\n",
     "    optim = pybop.GradientDescent(cost, sigma0=sigma, max_iterations=100)\n",
-    "    x, final_cost = optim.run()\n",
+    "    results = optim.run()\n",
     "    optims.append(optim)\n",
-    "    xs.append(x)"
+    "    xs.append(results.x)"
    ]
   },
   {
@@ -1419,8 +1419,8 @@
    ],
    "source": [
     "optim = pybop.Optimisation(cost, optimiser=pybop.GradientDescent, sigma0=0.08)\n",
-    "x, final_cost = optim.run()\n",
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "results = optim.run()\n",
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {

examples/notebooks/optimiser_interface.ipynb

@@ -167,7 +167,7 @@
     "optim_one.set_max_iterations(\n",
     "    50\n",
     ")  # Alternative set() / get() methods for PINTS optimisers\n",
-    "x1, final_cost = optim_one.run()"
+    "results = optim_one.run()"
    ]
   },
   {
@@ -191,7 +191,7 @@
     "optim_two.set_max_iterations(\n",
     "    50\n",
     ")  # Alternative set() / get() methods for PINTS optimisers\n",
-    "x2, final_cost = optim_two.run()"
+    "results2 = optim_two.run()"
    ]
   },
   {
@@ -247,8 +247,8 @@
     }
    ],
    "source": [
-    "print(\"Estimated parameters x1:\", x1)\n",
-    "print(\"Estimated parameters x2:\", x2)"
+    "print(\"Estimated parameters x1:\", results.x)\n",
+    "print(\"Estimated parameters x2:\", results2.x)"
    ]
   },
   {

examples/notebooks/pouch_cell_identification.ipynb

@@ -328,7 +328,7 @@
    },
    "outputs": [],
    "source": [
-    "x, final_cost = optim.run()"
+    "results = optim.run()"
    ]
   },
   {
@@ -365,7 +365,7 @@
     }
    ],
    "source": [
-    "x  # This will output the estimated parameters"
+    "results.x  # This will output the estimated parameters"
    ]
   },
   {
@@ -461,7 +461,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -513,7 +513,7 @@
     }
    ],
    "source": [
-    "sol = problem.evaluate(parameters.as_dict(x))\n",
+    "sol = problem.evaluate(parameters.as_dict(results.x))\n",
     "\n",
     "go.Figure(\n",
     "    [\n",

examples/notebooks/single_pulse_circuit_model.ipynb

@@ -483,9 +483,9 @@
    ],
    "source": [
     "optim = pybop.PSO(cost, max_unchanged_iterations=55, threshold=1e-6)\n",
-    "x, final_cost = optim.run()\n",
+    "results = optim.run()\n",
     "print(\"Initial parameters:\", optim.x0)\n",
-    "print(\"Estimated parameters:\", x)"
+    "print(\"Estimated parameters:\", results.x)"
    ]
   },
   {
@@ -539,7 +539,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Optimised Comparison\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Optimised Comparison\");"
    ]
   },
   {
@@ -746,7 +746,7 @@
     }
    ],
    "source": [
-    "pybop.quick_plot(problem, problem_inputs=x, title=\"Parameter Extrapolation\");"
+    "pybop.quick_plot(problem, problem_inputs=results.x, title=\"Parameter Extrapolation\");"
    ]
   },
   {