Added tests for #1691.

pints/tests/test_mcmc_controller.py

@@ -690,6 +690,26 @@ def test_log_pdf_storage_in_memory_single(self):
             likelihoods = [self.log_likelihood(x) for x in chain]
             self.assertTrue(np.all(evals[i] == likelihoods))
 
+        # Test with a sensitivity-using method
+        mcmc = pints.MCMCController(
+            self.log_posterior, n_chains, xs, method=pints.MALAMCMC)
+        mcmc.set_max_iterations(n_iterations)
+        mcmc.set_log_to_screen(False)
+        mcmc.set_log_pdf_storage(True)
+        chains = mcmc.run()
+        evals = mcmc.log_pdfs()
+        self.assertEqual(len(evals.shape), 3)
+        self.assertEqual(evals.shape[0], n_chains)
+        self.assertEqual(evals.shape[1], n_iterations)
+        self.assertEqual(evals.shape[2], 3)
+        for i, chain in enumerate(chains):
+            posteriors = [self.log_posterior(x) for x in chain]
+            self.assertTrue(np.all(evals[i, :, 0] == posteriors))
+            likelihoods = [self.log_likelihood(x) for x in chain]
+            self.assertTrue(np.all(evals[i, :, 1] == likelihoods))
+            priors = [self.log_prior(x) for x in chain]
+            self.assertTrue(np.all(evals[i, :, 2] == priors))
+
         # Test disabling again
         mcmc = pints.MCMCController(self.log_posterior, n_chains, xs)
         mcmc.set_max_iterations(n_iterations)
@@ -706,7 +726,9 @@ def test_log_pdf_storage_in_memory_multi(self):
         x0 = np.array(self.real_parameters) * 1.05
         x1 = np.array(self.real_parameters) * 1.15
         x2 = np.array(self.real_parameters) * 0.95
-        xs = [x0, x1, x2]
+        x3 = np.array(self.real_parameters) * 0.95
+        x4 = np.array(self.real_parameters) * 0.95
+        xs = [x0, x1, x2, x3, x4]
         n_chains = len(xs)
         n_iterations = 100
         meth = pints.DifferentialEvolutionMCMC
@@ -737,6 +759,16 @@ def test_log_pdf_storage_in_memory_multi(self):
             priors = [self.log_prior(x) for x in chain]
             self.assertTrue(np.all(evals[i, :, 2] == priors))
 
+        # Test with a sensitivity-using method
+        # We don't have any of these!
+        mcmc = pints.MCMCController(
+            self.log_posterior, n_chains, xs,
+            method=FakeMultiChainSamplerWithSensitivities)
+        mcmc.set_max_iterations(5)
+        mcmc.set_log_to_screen(False)
+        mcmc.set_log_pdf_storage(True)
+        chains = mcmc.run()
+
         # Test with a loglikelihood
         mcmc = pints.MCMCController(
             self.log_likelihood, n_chains, xs, method=meth)
@@ -1651,6 +1683,26 @@ def tell(self, fx):
         return None if x is None else (x, fx, np.array([True] * self._n))
 
 
+class FakeMultiChainSamplerWithSensitivities(pints.MultiChainMCMC):
+    """
+    Fake sampler that pretends to be a multi-chain method that uses
+    sensitivities. At the moment (2024-12-18) we don't have these in PINTS, but
+    we need to check that code potentially handling this type of sampler exists
+    or raises not-implemented errors.
+    """
+    def ask(self):
+        self._xs = self._x0 + 1e-3 * np.random.uniform(
+            size=(self._n_chains, self._n_parameters))
+        return self._xs
+
+    def current_log_pdfs(self):
+        return self._fxs
+
+    def tell(self, fxs):
+        self._fxs = fxs
+        return self._xs, self._fxs, [True] * self._n_chains
+
+
 class TestMCMCControllerSingleChainStorage(unittest.TestCase):
     """
     Tests storage of samples and evaluations to disk, running with a