test(integ): Adjusts sigma0, adds coverage for prior updt

Add tests for the prior cls update and SciPyMinimize resampling.
Also updates sigma0 for GradientDescent and loosens assertion

pybop/__init__.py

@@ -119,7 +119,7 @@
 #
 from .parameters.parameter import Parameter
 from .parameters.parameter_set import ParameterSet
-from .parameters.priors import Gaussian, Uniform, Exponential
+from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential
 
 
 #

pybop/optimisers/scipy_optimisers.py

@@ -66,7 +66,9 @@ def callback(x):
                 if not np.isinf(self.cost0):
                     break
             if np.isinf(self.cost0):
-                raise Exception("The initial parameter values return an infinite cost.")
+                raise ValueError(
+                    "The initial parameter values return an infinite cost."
+                )
 
         # Scale the cost function and eliminate nan values
         self.inf_count = 0

tests/integration/test_parameterisations.py

@@ -120,7 +120,7 @@ def test_spm_optimisers(self, optimiser, spm_costs):
                 )
             else:
                 parameterisation = pybop.Optimisation(
-                    cost=spm_costs, optimiser=optimiser, sigma0=0.02
+                    cost=spm_costs, optimiser=optimiser, sigma0=0.035
                 )
         elif optimiser in [pybop.SciPyMinimize]:
             parameterisation = pybop.Optimisation(
@@ -148,7 +148,7 @@ def test_spm_optimisers(self, optimiser, spm_costs):
             else:
                 np.testing.assert_allclose(x, self.ground_truth, atol=3.0e-2)
         else:
-            if optimiser in [pybop.SciPyMinimize]:
+            if optimiser in [pybop.GradientDescent, pybop.SciPyMinimize]:
                 np.testing.assert_allclose(x, self.ground_truth, atol=2.5e-2)
             else:
                 np.testing.assert_allclose(x, self.ground_truth, atol=1.75e-2)

tests/unit/test_optimisation.py

@@ -54,7 +54,6 @@ def cost(self, model, one_parameter, dataset):
             model,
             one_parameter,
             dataset,
-            signal=["Voltage [V]"],
         )
         return pybop.SumSquaredError(problem)
 
@@ -64,7 +63,6 @@ def two_param_cost(self, model, two_parameters, dataset):
             model,
             two_parameters,
             dataset,
-            signal=["Voltage [V]"],
         )
         return pybop.SumSquaredError(problem)
 
@@ -142,6 +140,41 @@ def test_prior_sampling(self, cost):
 
             assert opt.x0 <= 0.62 and opt.x0 >= 0.58
 
+    @pytest.mark.unit
+    @pytest.mark.parametrize(
+        "mean, sigma, expect_exception",
+        [
+            (0.85, 0.2, False),
+            (0.85, 0.001, True),
+        ],
+    )
+    def test_scipy_prior_resampling(
+        self, model, dataset, mean, sigma, expect_exception
+    ):
+        # Set up the parameter with a Gaussian prior
+        parameter = pybop.Parameter(
+            "Negative electrode active material volume fraction",
+            prior=pybop.Gaussian(mean, sigma),
+            bounds=[0.55, 0.95],
+        )
+
+        # Define the problem and cost
+        problem = pybop.FittingProblem(model, [parameter], dataset)
+        cost = pybop.SumSquaredError(problem)
+
+        # Create the optimisation class with infeasible solutions disabled
+        opt = pybop.Optimisation(
+            cost=cost, optimiser=pybop.SciPyMinimize, allow_infeasible_solutions=False
+        )
+        opt.set_max_iterations(1)
+
+        # If small sigma, expect a ValueError due inability to resample a non np.inf cost
+        if expect_exception:
+            with pytest.raises(ValueError):
+                opt.run()
+        else:
+            opt.run()
+
     @pytest.mark.unit
     def test_halting(self, cost):
         # Test max evalutions

tests/unit/test_priors.py

@@ -21,6 +21,11 @@ def Uniform(self):
     def Exponential(self):
         return pybop.Exponential(scale=1)
 
+    @pytest.mark.unit
+    def test_base_prior(self):
+        base = pybop.BasePrior()
+        assert isinstance(base, pybop.BasePrior)
+
     @pytest.mark.unit
     def test_priors(self, Gaussian, Uniform, Exponential):
         # Test pdf
@@ -49,6 +54,13 @@ def test_gaussian_rvs(self, Gaussian):
         assert abs(mean - 0.5) < 0.2
         assert abs(std - 1) < 0.2
 
+    @pytest.mark.unit
+    def test_incorrect_rvs(self, Gaussian):
+        with pytest.raises(ValueError):
+            Gaussian.rvs(size="a")
+        with pytest.raises(ValueError):
+            Gaussian.rvs(size=(1, 2, -1))
+
     @pytest.mark.unit
     def test_uniform_rvs(self, Uniform):
         samples = Uniform.rvs(size=500)