Merge pull request #321 from pybop-team/320-bug-fix-integration-tests…

…-post-304

Fixes #320

CHANGELOG.md

@@ -2,6 +2,7 @@
 
 ## Features
 
+- [#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.
 - [#301](https://github.com/pybop-team/PyBOP/pull/301) - Updates default echem solver to "fast with events" mode.
@@ -19,6 +20,7 @@ codesigned binaries and source distributions via `sigstore-python`.
 
 ## Bug Fixes
 
+- [#321](https://github.com/pybop-team/PyBOP/pull/321) - Improves `integration/test_spm_parameterisation.py` stability, adds flakly pytest plugin, and `test_thevenin_parameterisation.py` integration test.
 - [#330](https://github.com/pybop-team/PyBOP/issues/330) - Fixes implementation of default plotting options.
 - [#317](https://github.com/pybop-team/PyBOP/pull/317) - Installs seed packages into `nox` sessions, ensuring that scheduled tests can pass.
 - [#308](https://github.com/pybop-team/PyBOP/pull/308) - Enables testing on both macOS Intel and macOS ARM (Silicon) runners and fixes the scheduled tests.

pybop/__init__.py

@@ -118,7 +118,7 @@
 #
 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

@@ -25,6 +25,7 @@ def __init__(self, method=None, bounds=None, maxiter=None, tol=1e-5):
         super().__init__()
         self.method = method
         self.bounds = bounds
+        self.num_resamples = 40
         self.tol = tol
         self.options = {}
         self._max_iterations = maxiter
@@ -56,11 +57,21 @@ def _runoptimise(self, cost_function, x0):
         def callback(x):
             self.log.append([x])
 
-        # Scale the cost function and eliminate nan values
+        # Check x0 and resample if required
         self.cost0 = cost_function(x0)
-        self.inf_count = 0
         if np.isinf(self.cost0):
-            raise Exception("The initial parameter values return an infinite cost.")
+            for i in range(1, self.num_resamples):
+                x0 = cost_function.problem.sample_initial_conditions(seed=i)
+                self.cost0 = cost_function(x0)
+                if not np.isinf(self.cost0):
+                    break
+            if np.isinf(self.cost0):
+                raise ValueError(
+                    "The initial parameter values return an infinite cost."
+                )
+
+        # Scale the cost function and eliminate nan values
+        self.inf_count = 0
 
         def cost_wrapper(x):
             cost = cost_function(x) / self.cost0

pybop/parameters/parameter.py

@@ -42,7 +42,7 @@ def __init__(
         self.set_bounds(bounds)
         self.margin = 1e-4
 
-    def rvs(self, n_samples):
+    def rvs(self, n_samples, random_state=None):
         """
         Draw random samples from the parameter's prior distribution.
 
@@ -59,7 +59,7 @@ def rvs(self, n_samples):
         array-like
             An array of samples drawn from the prior distribution within the parameter's bounds.
         """
-        samples = self.prior.rvs(n_samples)
+        samples = self.prior.rvs(n_samples, random_state=random_state)
 
         # Constrain samples to be within bounds
         if self.bounds is not None: