Add check to CMAES and make XNES default optimiser (#253)

* Add CMAES check and make XNES default * Update CMAES error message Co-authored-by: Brady Planden <[email protected]>
pybop-team · Mar 27, 2024 · 7b500e4 · 7b500e4
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -5,6 +5,8 @@
 
 ## Bug Fixes
 
+- [#91](https://github.com/pybop-team/PyBOP/issues/91) - Adds a check on the number of parameters for CMAES and makes XNES the default optimiser.
+
 # [v24.3](https://github.com/pybop-team/PyBOP/tree/v24.3) - 2024-03-25
 
 ## Features

diff --git a/pybop/_optimisation.py b/pybop/_optimisation.py
@@ -86,7 +86,7 @@ def __init__(
         self.pints = True
 
         if self.optimiser is None:
-            self.optimiser = pybop.CMAES
+            self.optimiser = pybop.XNES
         elif issubclass(self.optimiser, pints.Optimiser):
             pass
         else:

diff --git a/pybop/optimisers/pints_optimisers.py b/pybop/optimisers/pints_optimisers.py
@@ -217,6 +217,11 @@ class CMAES(pints.CMAES):
     """
 
     def __init__(self, x0, sigma0=0.1, bounds=None):
+        if len(x0) == 1:
+            raise ValueError(
+                "CMAES requires optimisation of >= 2 parameters at once. "
+                + "Please choose another optimiser."
+            )
         if bounds is not None:
             self.boundaries = pints.RectangularBoundaries(
                 bounds["lower"], bounds["upper"]

diff --git a/tests/unit/test_optimisation.py b/tests/unit/test_optimisation.py
@@ -15,12 +15,12 @@ def dataset(self):
             {
                 "Time [s]": np.linspace(0, 360, 10),
                 "Current function [A]": np.zeros(10),
-                "Terminal voltage [V]": np.ones(10),
+                "Voltage [V]": np.ones(10),
             }
         )
 
     @pytest.fixture
-    def parameters(self):
+    def one_parameter(self):
         return [
             pybop.Parameter(
                 "Negative electrode active material volume fraction",
@@ -30,17 +30,42 @@ def parameters(self):
         ]
 
     @pytest.fixture
-    def problem(self, parameters, dataset):
-        model = pybop.lithium_ion.SPM()
-        return pybop.FittingProblem(
+    def two_parameters(self):
+        return [
+            pybop.Parameter(
+                "Negative electrode active material volume fraction",
+                prior=pybop.Gaussian(0.6, 0.2),
+                bounds=[0.58, 0.62],
+            ),
+            pybop.Parameter(
+                "Positive electrode active material volume fraction",
+                prior=pybop.Gaussian(0.55, 0.05),
+                bounds=[0.53, 0.57],
+            ),
+        ]
+
+    @pytest.fixture
+    def model(self):
+        return pybop.lithium_ion.SPM()
+
+    @pytest.fixture
+    def cost(self, model, one_parameter, dataset):
+        problem = pybop.FittingProblem(
             model,
-            parameters,
+            one_parameter,
             dataset,
-            signal=["Terminal voltage [V]"],
+            signal=["Voltage [V]"],
         )
+        return pybop.SumSquaredError(problem)
 
     @pytest.fixture
-    def cost(self, problem):
+    def two_param_cost(self, model, two_parameters, dataset):
+        problem = pybop.FittingProblem(
+            model,
+            two_parameters,
+            dataset,
+            signal=["Voltage [V]"],
+        )
         return pybop.SumSquaredError(problem)
 
     @pytest.mark.parametrize(
@@ -58,7 +83,9 @@ def cost(self, problem):
         ],
     )
     @pytest.mark.unit
-    def test_optimiser_classes(self, cost, optimiser_class, expected_name):
+    def test_optimiser_classes(self, two_param_cost, optimiser_class, expected_name):
+        # Test class construction
+        cost = two_param_cost
         opt = pybop.Optimisation(cost=cost, optimiser=optimiser_class)
 
         assert opt.optimiser is not None
@@ -77,13 +104,19 @@ def test_optimiser_classes(self, cost, optimiser_class, expected_name):
             else:
                 assert opt.optimiser.boundaries is None
 
+    @pytest.mark.unit
+    def test_single_parameter(self, cost):
+        # Test catch for optimisers that can only run with multiple parameters
+        with pytest.raises(
+            ValueError,
+            match=r"requires optimisation of >= 2 parameters at once.",
+        ):
+            pybop.Optimisation(cost=cost, optimiser=pybop.CMAES)
+
     @pytest.mark.unit
     def test_default_optimiser(self, cost):
         opt = pybop.Optimisation(cost=cost)
-        assert (
-            opt.optimiser.name()
-            == "Covariance Matrix Adaptation Evolution Strategy (CMA-ES)"
-        )
+        assert opt.optimiser.name() == "Exponential Natural Evolution Strategy (xNES)"
 
     @pytest.mark.unit
     def test_incorrect_optimiser_class(self, cost):
@@ -97,7 +130,7 @@ class RandomClass:
     def test_prior_sampling(self, cost):
         # Tests prior sampling
         for i in range(50):
-            opt = pybop.Optimisation(cost=cost, optimiser=pybop.CMAES)
+            opt = pybop.Optimisation(cost=cost)
 
             assert opt.x0 <= 0.62 and opt.x0 >= 0.58