Test MinlipSurvivalAnalysis with callable kernel

tests/test_minlip.py

@@ -7,6 +7,7 @@
 from sksurv.column import encode_categorical
 from sksurv.datasets import load_gbsg2
 from sksurv.exceptions import NoComparablePairException
+from sksurv.kernels import ClinicalKernelTransform
 from sksurv.svm._minlip import create_difference_matrix
 from sksurv.svm.minlip import HingeLossSurvivalSVM, MinlipSurvivalAnalysis
 from sksurv.testing import FixtureParameterFactory, assert_cindex_almost_equal
@@ -396,6 +397,22 @@ def test_kernel_precomputed(gbsg2_scaled, solver):
         p = m.predict(X_test)
         assert_cindex_almost_equal(y_test["cens"], y_test["time"], p, (0.6518928901200369, 8472, 4524, 0, 3))
 
+    @staticmethod
+    @pytest.mark.slow()
+    def test_fit_clinical_kernel(make_whas500):
+        whas500 = make_whas500(with_mean=False, with_std=False)
+
+        trans = ClinicalKernelTransform()
+        trans.fit(whas500.x_data_frame)
+
+        m = MinlipSurvivalAnalysis(kernel=trans.pairwise_kernel)
+        m.fit(whas500.x, whas500.y)
+
+        assert not m.__sklearn_tags__().input_tags.pairwise
+
+        c = m.score(whas500.x, whas500.y)
+        assert c == pytest.approx(0.7314135916645598)
+
     @staticmethod
     @pytest.mark.parametrize("solver", ["osqp", "ecos"])
     def test_max_iter(gbsg2_scaled, solver):