Rename Sobol sensitivity method

pybop/analysis/sensitivity_analysis.py

@@ -1,13 +1,14 @@
 from typing import TYPE_CHECKING
 
+import numpy as np
 from SALib.analyze import sobol
 from SALib.sample.sobol import sample
 
 if TYPE_CHECKING:
     from pybop.problems.problem import Problem
 
 
-def sensitivity_analysis(
+def get_sobol_sensitivities(
     problem: "Problem", n_samples: int = 256, calc_second_order: bool = False
 ) -> dict:
     """
@@ -36,9 +37,13 @@ def sensitivity_analysis(
     Sensitivities : dict
     """
 
+    bounds_array = problem.parameters.get_bounds_array()
+    if not np.isfinite(bounds_array).all():
+        raise ValueError("SOBOL analysis requires finite bounds.")
+
     salib_dict = {
         "names": problem.parameters.names,
-        "bounds": problem.parameters.get_bounds_array(),
+        "bounds": bounds_array,
         "num_vars": len(problem.parameters),
     }
 

pybop/problems/problem.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from pybop.analysis.sensitivity_analysis import sensitivity_analysis
+from pybop.analysis.sensitivity_analysis import get_sobol_sensitivities
 from pybop.costs.base_cost import BaseCost
 from pybop.costs.evaluation import Evaluation
 from pybop.parameters.parameter import Inputs, Parameters
@@ -260,7 +260,7 @@ def get_finite_initial_cost(self):
             raise ValueError("The initial parameter values return an infinite cost.")
         return cost0
 
-    def sensitivity_analysis(
+    def get_sobol_sensitivities(
         self, n_samples: int = 256, calc_second_order: bool = False
     ) -> dict:
         """
@@ -275,7 +275,7 @@ def sensitivity_analysis(
         calc_second_order : bool, optional
             Whether to calculate second-order sensitivities.
         """
-        return sensitivity_analysis(
+        return get_sobol_sensitivities(
             problem=self, n_samples=n_samples, calc_second_order=calc_second_order
         )
 

tests/unit/test_problem.py

@@ -22,18 +22,10 @@ def model(self):
     def parameters(self):
         return {
             "Negative particle radius [m]": pybop.Parameter(
-                distribution=pybop.Gaussian(
-                    2e-05,
-                    0.1e-5,
-                    truncated_at=[1e-6, 5e-5],
-                )
+                distribution=pybop.Gaussian(2e-05, 0.1e-5, truncated_at=[1e-6, 5e-5])
             ),
             "Positive particle radius [m]": pybop.Parameter(
-                distribution=pybop.Gaussian(
-                    0.5e-05,
-                    0.1e-5,
-                    truncated_at=[1e-6, 5e-5],
-                )
+                distribution=pybop.Gaussian(0.5e-05, 0.1e-5, truncated_at=[1e-6, 5e-5])
             ),
         }
 
@@ -239,7 +231,7 @@ def test_parameter_sensitivities(self, simulator, dataset):
         cost = pybop.MeanAbsoluteError(dataset)
         problem = pybop.Problem(simulator, cost)
         n_params = len(problem.parameters)
-        result = problem.sensitivity_analysis(4, calc_second_order=True)
+        result = problem.get_sobol_sensitivities(4, calc_second_order=True)
 
         # Assertions
         assert isinstance(result, dict)
@@ -253,3 +245,9 @@ def test_parameter_sensitivities(self, simulator, dataset):
         assert isinstance(result["S2_conf"], np.ndarray)
         assert result["S1"].shape == (n_params,)
         assert result["ST"].shape == (n_params,)
+
+        problem.parameters["Negative particle radius [m]"] = pybop.Parameter(
+            distribution=pybop.Gaussian(2e-05, 0.1e-5)  # unbounded
+        )
+        with pytest.raises(ValueError, match="SOBOL analysis requires finite bounds."):
+            problem.get_sobol_sensitivities(4, calc_second_order=True)