FD objective with fixed parameters fix

pypesto/objective/finite_difference.py

@@ -327,6 +327,7 @@ def __init__(
         self.delta_grad: FDDelta = to_delta(delta_grad)
         self.delta_res: FDDelta = to_delta(delta_res)
         self.method: str = method
+        self.pre_post_processor = obj.pre_post_processor
 
         if method not in FD.METHODS:
             raise ValueError(

test/base/test_objective.py

@@ -342,12 +342,20 @@ def fd_delta(request):
     return request.param
 
 
-def test_fds(fd_method, fd_delta):
+# add a fixture for fixed and unfixed parameters
+@pytest.mark.parametrize("fixed", [True, False])
+def test_fds(fd_method, fd_delta, fixed):
     """Test finite differences."""
     problem = CRProblem()
 
-    # reference objective
-    obj = problem.get_objective()
+    if fixed:
+        fixed_problem = problem.get_problem()
+        fixed_problem.fix_parameters([1], problem.p_true[1])
+        obj = fixed_problem.objective
+        p = problem.p_true[0]
+    else:
+        obj = problem.get_objective()
+        p = problem.p_true
 
     # FDs for everything
     obj_fd = pypesto.FD(
@@ -394,7 +402,6 @@ def test_fds(fd_method, fd_delta):
         delta_grad=fd_delta,
         delta_res=fd_delta,
     )
-    p = problem.p_true
 
     # check that function values coincide (call delegated)
     for attr in ["fval", "res"]:

test/hierarchical/test_censored.py

@@ -88,7 +88,7 @@ def calculate(problem, x_dct):
         problem.objective,
     )
     finite_differences_results = finite_differences(
-        petab_problem.x_nominal_scaled,
+        petab_problem.x_nominal_free_scaled,
         (
             0,
             1,
@@ -100,7 +100,7 @@ def calculate(problem, x_dct):
     # with finite differences.
     assert np.allclose(
         finite_differences_results[1],
-        calculator_result["grad"],
+        calculator_result["grad"][petab_problem.x_free_indices],
     )
 
 

test/hierarchical/test_ordinal.py

@@ -175,7 +175,7 @@ def inner_calculate(problem, x_dct):
         problem.objective,
     )
     finite_differences_results = finite_differences(
-        petab_problem.x_nominal_scaled,
+        petab_problem.x_nominal_free_scaled,
         (
             0,
             1,
@@ -209,7 +209,7 @@ def inner_calculate(problem, x_dct):
     # with finite differences.
     assert np.allclose(
         finite_differences_results[1],
-        calculator_results[STANDARD]["grad"],
+        calculator_results[STANDARD]["grad"][petab_problem.x_free_indices],
     )
 
     # Since the nominal parameters are close to true ones,

test/hierarchical/test_spline.py

@@ -172,7 +172,7 @@ def inner_calculate(problem, x_dct):
 
     finite_differences = pypesto.objective.FD(problem.objective)
     FD_results = finite_differences(
-        x=petab_problem.x_nominal_scaled,
+        x=petab_problem.x_nominal_free_scaled,
         sensi_orders=(0, 1),
         mode=MODE_FUN,
     )
@@ -210,7 +210,9 @@ def inner_calculate(problem, x_dct):
     # The gradient should be close to the one calculated using
     # finite differences.
     assert np.allclose(
-        calculator_results["minimal_diff_on"]["grad"],
+        calculator_results["minimal_diff_on"]["grad"][
+            petab_problem.x_free_indices
+        ],
         FD_results[1],
         atol=atol,
     )