Non-regression test for scikit-learn#13393 (amg solver stability)

sklearn/cluster/tests/test_spectral.py

@@ -230,6 +230,23 @@ def test_spectral_clustering_with_arpack_amg_solvers():
                                 random_state=0)
 
 
+def test_spectral_clustering_amg_solver_failure():
+    # Non-regression test for amg solver failure (issue #13393 on github)
+    pytest.importorskip('pyamg')
+    seed = 36
+    num_nodes = 1000
+    X = sparse.rand(num_nodes, num_nodes, density=0.1, random_state=seed)
+    upper = sparse.triu(X) - sparse.diags(X.diagonal())
+    sym_matrix = upper + upper.T
+    clustering = SpectralClustering(n_clusters=3,
+                                    eigen_solver='amg',
+                                    random_state=seed,
+                                    affinity='precomputed')
+
+    # Smoke test: underlying solver should not raise numpy.linalg.LinAlgError
+    clustering.fit(sym_matrix)
+
+
 def test_n_components():
     # Test that after adding n_components, result is different and
     # n_components = n_clusters by default