chore: ligrec add explicit reproducibility tests

src/squidpy/gr/_ligrec.py

@@ -641,7 +641,17 @@ def ligrec(
     copy: bool = False,
     key_added: str | None = None,
     gene_symbols: str | None = None,
-    **kwargs: Any,
+    n_perms: int = 1000,
+    seed: int | None = None,
+    clusters: Cluster_t | None = None,
+    alpha: float = 0.05,
+    numba_parallel: bool | None = None,
+    n_jobs: int | None = None,
+    backend: str = "loky",
+    show_progress_bar: bool = True,
+    interactions_params: Mapping[str, Any] = MappingProxyType({}),
+    transmitter_params: Mapping[str, Any] = MappingProxyType({"categories": "ligand"}),
+    receiver_params: Mapping[str, Any] = MappingProxyType({"categories": "receptor"}),
 ) -> Mapping[str, pd.DataFrame] | None:
     """
     %(PT_test.full_desc)s
@@ -663,15 +673,28 @@ def ligrec(
     with _genesymbols(adata, key=gene_symbols, use_raw=use_raw, make_unique=False):
         return (  # type: ignore[no-any-return]
             PermutationTest(adata, use_raw=use_raw)
-            .prepare(interactions, complex_policy=complex_policy, **kwargs)
+            .prepare(
+                interactions,
+                complex_policy=complex_policy,
+                interactions_params=interactions_params,
+                transmitter_params=transmitter_params,
+                receiver_params=receiver_params,
+            )
             .test(
                 cluster_key=cluster_key,
+                clusters=clusters,
+                n_perms=n_perms,
                 threshold=threshold,
+                seed=seed,
                 corr_method=corr_method,
                 corr_axis=corr_axis,
+                alpha=alpha,
                 copy=copy,
                 key_added=key_added,
-                **kwargs,
+                numba_parallel=numba_parallel,
+                n_jobs=n_jobs,
+                backend=backend,
+                show_progress_bar=show_progress_bar,
             )
         )
 

tests/conftest.py

@@ -263,6 +263,12 @@ def ligrec_no_numba() -> Mapping[str, pd.DataFrame]:
         return {"means": data[0], "pvalues": data[1], "metadata": data[2]}
 
 
+@pytest.fixture(scope="session")
+def ligrec_pvalues_reference() -> Mapping[str, pd.DataFrame]:
+    with open("tests/_data/ligrec_pvalues_reference.pickle", "rb") as fin:
+        return pickle.load(fin)
+
+
 @pytest.fixture(scope="session")
 def ligrec_result() -> Mapping[str, pd.DataFrame]:
     adata = _adata.copy()

tests/graph/test_ligrec.py

@@ -379,6 +379,23 @@ def test_reproducibility_numba_off(
         np.testing.assert_allclose(r["pvalues"], ligrec_no_numba["pvalues"])
         np.testing.assert_array_equal(np.where(np.isnan(r["pvalues"])), np.where(np.isnan(ligrec_no_numba["pvalues"])))
 
+    def test_pvalues_reference(
+        self, adata: AnnData, interactions: Interactions_t, ligrec_pvalues_reference: Mapping[str, pd.DataFrame]
+    ):
+        r = ligrec(
+            adata, _CK, interactions=interactions, n_perms=25, copy=True, show_progress_bar=False, seed=42, n_jobs=1
+        )
+        np.testing.assert_array_equal(r["means"].index, ligrec_pvalues_reference["means"].index)
+        np.testing.assert_array_equal(r["means"].columns, ligrec_pvalues_reference["means"].columns)
+        np.testing.assert_array_equal(r["pvalues"].index, ligrec_pvalues_reference["pvalues"].index)
+        np.testing.assert_array_equal(r["pvalues"].columns, ligrec_pvalues_reference["pvalues"].columns)
+
+        np.testing.assert_allclose(r["means"], ligrec_pvalues_reference["means"])
+        np.testing.assert_allclose(r["pvalues"], ligrec_pvalues_reference["pvalues"])
+        np.testing.assert_array_equal(
+            np.where(np.isnan(r["pvalues"])), np.where(np.isnan(ligrec_pvalues_reference["pvalues"]))
+        )
+
     def test_logging(self, adata: AnnData, interactions: Interactions_t, capsys):
         s.logfile = sys.stderr
         s.verbosity = 4