Add basic iteration tests

tests/test_iterations.py

@@ -5,6 +5,7 @@
 import pytest
 
 from baybe.acquisition.base import AcquisitionFunction
+from baybe.kernels import MaternKernel, ScaleKernel
 from baybe.kernels.priors import (
     GammaPrior,
     HalfCauchyPrior,
@@ -128,6 +129,20 @@
     SmoothedBoxPrior(0, 3, 0.1),
 ]
 
+# Note that this test does not vary the priors as there is currently a separate test for
+# this aspect.
+valid_base_kernels = [MaternKernel(nu=nu) for nu in (0.5, 1.5, 2.5)]
+
+# Due to numerical issues (i.e., matrix systems not being solvable) we do not test
+# different priors here.
+valid_scale_kernels = [
+    ScaleKernel(base_kernel=base_kernel, outputscale_prior=prior)
+    for base_kernel in valid_base_kernels
+    for prior in valid_priors
+]
+
+valid_kernels = valid_base_kernels + valid_scale_kernels
+
 test_targets = [
     ["Target_max"],
     ["Target_min"],
@@ -169,6 +184,17 @@ def test_iter_prior(campaign, n_iterations, batch_size):
     run_iterations(campaign, n_iterations, batch_size)
 
 
+# For these tests, there were numerical issues without restricting the batch size to 1
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "kernel", valid_kernels, ids=[c.__class__ for c in valid_kernels]
+)
+@pytest.mark.parametrize("n_iterations", [3], ids=["i3"])
+@pytest.mark.parametrize("batch_size", [1], ids=["b1"])
+def test_iter_kernel(campaign, n_iterations, batch_size):
+    run_iterations(campaign, n_iterations, batch_size)
+
+
 @pytest.mark.slow
 @pytest.mark.parametrize(
     "surrogate_model",