Update unit tests to latest changes

theislab · Apr 12, 2024 · d384414 · d384414
1 parent d93c077
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diff --git a/tests/preprocessing/test_moments.py b/tests/preprocessing/test_moments.py
@@ -204,7 +204,6 @@ def _compare_adatas(self, adata_1, adata_2):
         assert set(adata_1.uns["neighbors"]) == {
             "connectivities_key",
             "distances_key",
-            "indices",
             "params",
         }
         assert (
@@ -215,10 +214,6 @@ def _compare_adatas(self, adata_1, adata_2):
             adata_1.uns["neighbors"]["distances_key"]
             == adata_2.uns["neighbors"]["distances_key"]
         )
-        np.testing.assert_equal(
-            adata_1.uns["neighbors"]["indices"],
-            adata_2.uns["neighbors"]["indices"],
-        )
         assert adata_1.uns["neighbors"]["params"] == adata_2.uns["neighbors"]["params"]
 
     @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])

diff --git a/tests/preprocessing/test_neighbors.py b/tests/preprocessing/test_neighbors.py
@@ -17,7 +17,6 @@
     _get_scanpy_neighbors,
     _get_sklearn_neighbors,
     _set_pca,
-    compute_connectivities_umap,
     get_connectivities,
     get_csr_from_indices,
     get_duplicate_cells,
@@ -34,37 +33,6 @@
 from tests.core import get_adata
 
 
-class TestComputeConnectivitiesUmap:
-    @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])
-    @pytest.mark.parametrize("n_obs", [50, 100])
-    def test_real_data(self, adata, dataset, n_obs):
-        adata = adata(dataset=dataset, n_obs=n_obs, raw=False, preprocessed=True)
-
-        knn_indices = adata.uns["neighbors"]["indices"]
-
-        knn_distances = []
-        for row_distance, row_index in zip(adata.obsp["distances"], knn_indices):
-            knn_distances.append(row_distance.A[0, row_index])
-        knn_distances = np.array(knn_distances)
-
-        distance_matrix, connectivity_matrix = compute_connectivities_umap(
-            knn_indices=knn_indices,
-            knn_dists=knn_distances,
-            n_obs=n_obs,
-            n_neighbors=adata.uns["neighbors"]["params"]["n_neighbors"],
-        )
-
-        assert isinstance(distance_matrix, csr_matrix)
-        np.testing.assert_almost_equal(
-            distance_matrix.A, adata.obsp["distances"].A, decimal=4
-        )
-
-        assert isinstance(connectivity_matrix, csr_matrix)
-        np.testing.assert_almost_equal(
-            connectivity_matrix.A, adata.obsp["connectivities"].A, decimal=4
-        )
-
-
 class TestGetConnectivities:
     @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])
     @pytest.mark.parametrize("n_obs", [50, 100])
@@ -274,28 +242,6 @@ def test_manual_data(
         assert isinstance(returned_matrix, csr_matrix)
         assert (returned_matrix != ground_truth).getnnz() == 0
 
-    @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])
-    @pytest.mark.parametrize("n_obs", [50, 100])
-    def test_real_data(self, adata, dataset, n_obs):
-        adata = adata(dataset=dataset, n_obs=n_obs, raw=False, preprocessed=True)
-
-        knn_indices = adata.uns["neighbors"]["indices"]
-
-        knn_distances = []
-        for row_distance, row_index in zip(adata.obsp["distances"], knn_indices):
-            knn_distances.append(row_distance.A[0, row_index])
-        knn_distances = np.array(knn_distances)
-
-        returned_matrix = get_csr_from_indices(
-            knn_indices=knn_indices,
-            knn_dists=knn_distances,
-            n_obs=n_obs,
-            n_neighbors=adata.uns["neighbors"]["params"]["n_neighbors"],
-        )
-
-        assert isinstance(returned_matrix, csr_matrix)
-        assert (returned_matrix != adata.obsp["distances"]).getnnz() == 0
-
 
 class TestGetDuplicateCells:
     @pytest.mark.parametrize(
@@ -1828,32 +1774,6 @@ def test_set_diagonal(self, knn_distances, knn_indices, remove_diag):
         np.testing.assert_equal(knn_indices_[:, 0], np.arange(3))
         np.testing.assert_equal(knn_indices_[:, 1:], knn_indices)
 
-    @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])
-    @pytest.mark.parametrize("n_obs", [50, 100])
-    @pytest.mark.parametrize("remove_diag", [True, False])
-    def test_real_data(self, adata, dataset, n_obs, remove_diag):
-        adata = adata(dataset=dataset, n_obs=n_obs, raw=False, preprocessed=True)
-        n_neighbors = adata.uns["neighbors"]["params"]["n_neighbors"]
-
-        knn_distances = adata.obsp["distances"][
-            np.repeat(np.arange(adata.n_obs), n_neighbors).reshape(adata.n_obs, -1),
-            adata.uns["neighbors"]["indices"],
-        ].A
-        knn_indices = adata.uns["neighbors"]["indices"]
-
-        knn_distances_, knn_indices_ = set_diagonal(
-            knn_distances=knn_distances,
-            knn_indices=knn_indices,
-            remove_diag=remove_diag,
-        )
-
-        if remove_diag:
-            np.testing.assert_equal(knn_distances_, knn_distances[:, 1:])
-            np.testing.assert_equal(knn_indices_, knn_indices[:, 1:])
-        else:
-            np.testing.assert_equal(knn_distances_, knn_distances)
-            np.testing.assert_equal(knn_indices_, knn_indices)
-
 
 class TestSetPCA:
     @pytest.mark.parametrize("dataset", ["pancreas", "dentategyrus"])

diff --git a/tests/test_basic.py b/tests/test_basic.py
@@ -13,17 +13,6 @@ def test_einsum():
     assert np.allclose(l2_norm(Ms), np.linalg.norm(Ms, axis=1))
 
 
-def test_neighbors():
-    adata = scv.datasets.simulation(random_seed=0, n_vars=100)
-    scv.pp.filter_and_normalize(adata)
-    scv.pp.pca(adata)
-    scv.pp.neighbors(adata)
-    adata_ = scv.pp.neighbors(adata, method="sklearn", copy=True)
-    dists = np.round(adata.obsp["distances"][0].data, 2)
-    dists_ = np.round(adata_.obsp["distances"][0].data, 2)
-    assert np.all(dists == dists_)
-
-
 def test_dynamical_model():
     adata = scv.datasets.simulation(random_seed=0, n_vars=10)
     scv.pp.filter_and_normalize(adata)