don't re-compute cost for voronoi plot

Author: SarahRo

papers/joss/param_plots.py

@@ -239,7 +239,7 @@
         print("True parameter values:", true_value)
 
         # Plot convergence
-        cost_log = result.cost
+        cost_log = result.cost_convergence
         iteration_numbers = list(range(1, len(cost_log) + 1))
         convergence_plot_dict = pybop.plot.StandardPlot(
             x=iteration_numbers,
@@ -318,7 +318,7 @@
         print("True parameter values:", true_value)
 
         # Plot convergence
-        cost_log = result.cost
+        cost_log = result.cost_convergence
         iteration_numbers = list(range(1, len(cost_log) + 1))
         convergence_plot_dict = pybop.plot.StandardPlot(
             x=iteration_numbers,

pybop/_result.py

@@ -45,8 +45,8 @@ def __init__(
         self._x_model = [logger.x_model]
         self._x0 = [logger.x0]
         self._best_cost = [logger.cost_best]
-        self._cost = [logger.cost_convergence]
-        self._initial_cost = [logger.cost[0]]
+        self._cost_convergence = [logger.cost_convergence]
+        self._cost = [logger.cost]
         self._n_iterations = [logger.iteration]
         self._iteration_number = [logger.iteration_number]
         self._n_evaluations = [logger.evaluations]
@@ -83,11 +83,12 @@ def combine(results: list["Result"]) -> "Result":
             for x in result._best_cost  # noqa: SLF001
         ]
         ret._cost = [x for result in results for x in result._cost]  # noqa: SLF001
-        ret._initial_cost = [  # noqa: SLF001
+        ret._cost_convergence = [  # noqa: SLF001
             x
             for result in results
-            for x in result._initial_cost  # noqa: SLF001
+            for x in result._cost_convergence  # noqa: SLF001
         ]
+
         ret._n_iterations = [  # noqa: SLF001
             x
             for result in results
@@ -208,10 +209,18 @@ def cost(self) -> np.ndarray:
         """The log of the cost values."""
         return self._get_single_or_all("_cost")
 
+    @property
+    def cost_convergence(self) -> np.ndarray:
+        """The log of the cost convergence values."""
+        return self._get_single_or_all("_cost_convergence")
+
     @property
     def initial_cost(self) -> float:
         """The initial cost value(s)."""
-        return self._get_single_or_all("_initial_cost")
+        if len(self._cost) > 1:
+            return [c[0] for c in self._cost]
+        else:
+            return self._cost[0][0]
 
     @property
     def n_iterations(self) -> int:
@@ -346,7 +355,7 @@ def data_dict(self) -> dict:
             "x0": self._x0,
             "best_cost": self._best_cost,
             "cost": self._cost,
-            "initial_cost": self._initial_cost,
+            "cost_convergence": self._cost_convergence,
             "n_iterations": self._n_iterations,
             "iteration_number": self._iteration_number,
             "n_evaluations": self._n_evaluations,
@@ -442,13 +451,13 @@ def load_data(filename: str, file_format: str = "pickle") -> dict:
                 ("x_model", "x_model"),
                 ("x0", "x0"),
                 ("cost_best", "best_cost"),
-                ("cost_convergence", "cost"),
+                ("cost_convergence", "cost_convergence"),
+                ("cost", "cost"),
                 ("iteration", "n_iterations"),
                 ("iteration_number", "iteration_number"),
                 ("evaluations", "n_evaluations"),
             ]:
                 setattr(logger, logger_key, data[result_key][i])
-            logger.cost = [data["initial_cost"][i]]
 
             list_of_results.append(
                 Result(

pybop/plot/convergence.py

@@ -28,7 +28,7 @@ def convergence(result: "Result", show=True, **layout_kwargs):
     """
 
     # Extract log from the optimisation object
-    cost_log = result.cost
+    cost_log = result.cost_convergence
 
     # Generate a list of iteration numbers
     iteration_numbers = list(range(1, len(cost_log) + 1))

pybop/plot/voronoi.py

@@ -263,9 +263,7 @@ def surface(
         raise ValueError("This plot method requires two parameters.")
 
     x_optim, y_optim = map(list, zip(*points, strict=False))
-    f = result.problem.evaluate(
-        result.problem.parameters.to_inputs_list(points)
-    ).get_values()
+    f = result.cost
 
     # Translate bounds, taking only the first two elements
     xlim, ylim = (

tests/unit/test_optimisation.py

@@ -771,7 +771,8 @@ def compare_result_data(self, result1, result2):
         np.testing.assert_array_equal(result1._x0, result2._x0)
         np.testing.assert_array_equal(result1._best_cost, result2._best_cost)
         np.testing.assert_array_equal(result1._cost, result2._cost)
-        np.testing.assert_array_equal(result1._initial_cost, result2._initial_cost)
+        np.testing.assert_array_equal(result1._cost_convergence, result2._cost_convergence)
+        np.testing.assert_array_equal(result1.initial_cost, result2.initial_cost)
         np.testing.assert_array_equal(result1._n_iterations, result2._n_iterations)
         np.testing.assert_array_equal(
             result1._iteration_number, result2._iteration_number

tests/unit/test_sampling.py

@@ -309,7 +309,8 @@ def compare_result_data(self, result1, result2):
         np.testing.assert_array_equal(result1._x0, result2._x0)
         np.testing.assert_array_equal(result1._best_cost, result2._best_cost)
         np.testing.assert_array_equal(result1._cost, result2._cost)
-        np.testing.assert_array_equal(result1._initial_cost, result2._initial_cost)
+        np.testing.assert_array_equal(result1._cost_convergence, result2._cost_convergence)
+        np.testing.assert_array_equal(result1.initial_cost, result2.initial_cost)
         np.testing.assert_array_equal(result1._n_iterations, result2._n_iterations)
         np.testing.assert_array_equal(
             result1._iteration_number, result2._iteration_number