tests: increase coverage, remove redundant ValueError, sampler.chains…

… now optional arg with default=1
pybop-team · Aug 14, 2024 · 7a000cf · 7a000cf
1 parent e50812a
commit 7a000cf
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Showing 4 changed files with 29 additions and 36 deletions.
diff --git a/pybop/samplers/base_pints_sampler.py b/pybop/samplers/base_pints_sampler.py
@@ -24,8 +24,8 @@ class BasePintsSampler(BaseSampler):
     def __init__(
         self,
         log_pdf: LogPosterior,
-        chains: int,
         sampler,
+        chains: int = 1,
         warm_up=None,
         x0=None,
         cov0=0.1,
@@ -95,15 +95,12 @@ def __init__(
         self._single_chain = issubclass(sampler, SingleChainMCMC)
 
         # Construct the samplers object
-        try:
-            if self._single_chain:
-                self._n_samplers = self._n_chains
-                self._samplers = [sampler(x0, sigma0=self._cov0) for x0 in self._x0]
-            else:
-                self._n_samplers = 1
-                self._samplers = [sampler(self._n_chains, self._x0, self._cov0)]
-        except Exception as e:
-            raise ValueError(f"Error constructing samplers: {e}") from e
+        if self._single_chain:
+            self._n_samplers = self._n_chains
+            self._samplers = [sampler(x0, sigma0=self._cov0) for x0 in self._x0]
+        else:
+            self._n_samplers = 1
+            self._samplers = [sampler(self._n_chains, self._x0, self._cov0)]
 
         # Check for sensitivities from sampler and set evaluation
         self._needs_sensitivities = self._samplers[0].needs_sensitivities()

diff --git a/pybop/samplers/base_sampler.py b/pybop/samplers/base_sampler.py
@@ -37,12 +37,6 @@ def __init__(self, log_pdf: LogPosterior, x0, cov0: Union[np.ndarray, float]):
             else np.asarray(x0, dtype=float)
         )
 
-        # Validate x0 shape
-        if self._x0.ndim == 0:
-            raise ValueError(
-                f"x0 must be an array-like structure, but got a scalar: {x0}"
-            )
-
     def run(self) -> np.ndarray:
         """
         Sample from the posterior distribution.

diff --git a/pybop/samplers/pints_samplers.py b/pybop/samplers/pints_samplers.py
@@ -45,7 +45,9 @@ class NUTS(BasePintsSampler):
     """
 
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
-        super().__init__(log_pdf, chains, NoUTurnMCMC, x0=x0, cov0=cov0, **kwargs)
+        super().__init__(
+            log_pdf, NoUTurnMCMC, chains=chains, x0=x0, cov0=cov0, **kwargs
+        )
 
 
 class DREAM(BasePintsSampler):
@@ -73,7 +75,7 @@ class DREAM(BasePintsSampler):
     """
 
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
-        super().__init__(log_pdf, chains, PintsDREAM, x0=x0, cov0=cov0, **kwargs)
+        super().__init__(log_pdf, PintsDREAM, chains=chains, x0=x0, cov0=cov0, **kwargs)
 
 
 class AdaptiveCovarianceMCMC(BasePintsSampler):
@@ -101,8 +103,8 @@ class AdaptiveCovarianceMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsAdaptiveCovarianceMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -134,8 +136,8 @@ class DifferentialEvolutionMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsDifferentialEvolutionMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -168,8 +170,8 @@ class DramACMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsDramACMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -201,8 +203,8 @@ class EmceeHammerMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsEmceeHammerMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -234,8 +236,8 @@ class HaarioACMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsHaarioACMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -267,8 +269,8 @@ class HaarioBardenetACMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsHaarioBardenetACMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -300,8 +302,8 @@ class HamiltonianMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsHamiltonianMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -333,8 +335,8 @@ class MALAMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsMALAMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -366,8 +368,8 @@ class MetropolisRandomWalkMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsMetropolisRandomWalkMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -399,8 +401,8 @@ class MonomialGammaHamiltonianMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsMonomialGammaHamiltonianMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -432,8 +434,8 @@ class PopulationMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsPopulationMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -465,8 +467,8 @@ class RaoBlackwellACMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsRaoBlackwellACMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -498,8 +500,8 @@ class RelativisticMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsRelativisticMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -531,8 +533,8 @@ class SliceDoublingMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsSliceDoublingMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -564,8 +566,8 @@ class SliceRankShrinkingMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsSliceRankShrinkingMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,
@@ -597,8 +599,8 @@ class SliceStepoutMCMC(BasePintsSampler):
     def __init__(self, log_pdf, chains, x0=None, cov0=None, **kwargs):
         super().__init__(
             log_pdf,
-            chains,
             PintsSliceStepoutMCMC,
+            chains=chains,
             x0=x0,
             cov0=cov0,
             **kwargs,

diff --git a/tests/unit/test_sampling.py b/tests/unit/test_sampling.py
@@ -114,7 +114,7 @@ def multi_samplers(self):
         ],
     )
     @pytest.mark.unit
-    def test_initialization_and_run(
+    def test_initialisation_and_run(
         self, log_posterior, x0, chains, MCMC, multi_samplers
     ):
         sampler = pybop.MCMCSampler(
@@ -199,7 +199,7 @@ def test_multi_log_pdf(self, log_posterior, x0, chains):
             )
 
     @pytest.mark.unit
-    def test_invalid_initialization(self, log_posterior, x0):
+    def test_invalid_initialisation(self, log_posterior, x0):
         with pytest.raises(ValueError, match="Number of chains must be greater than 0"):
             AdaptiveCovarianceMCMC(
                 log_pdf=log_posterior,