Add tests

tests/graph/test_niche.py

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+from __future__ import annotations
+
+import numpy as np
+import pytest
+import scipy
+from anndata import AnnData
+from pandas.testing import assert_frame_equal
+from scipy.sparse import issparse
+
+from squidpy.gr import calculate_niche, spatial_neighbors
+from squidpy.gr._niche import _aggregate, _calculate_neighborhood_profile, _hop, _normalize, _setdiag, _utag
+
+SPATIAL_CONNECTIVITIES_KEY = "spatial_connectivities"
+N_NEIGHBORS = 20
+
+
+def test_neighborhood_profile_calculation(adata_seqfish: AnnData):
+    """Check whether niche calculation using neighborhood profile approach works as intended."""
+    spatial_neighbors(adata_seqfish, coord_type="generic", delaunay=False, n_neighs=N_NEIGHBORS)
+    calculate_niche(
+        adata_seqfish,
+        groups="celltype_mapped_refined",
+        flavor="neighborhood",
+        n_neighbors=N_NEIGHBORS,
+        resolutions=[0.1],
+        min_niche_size=100,
+    )
+    niches = adata_seqfish.obs["neighborhood_niche_res=0.1"]
+
+    # assert no nans, more niche labels than non-niche labels, and at least 100 obs per niche
+    assert niches.isna().sum() == 0
+    assert len(niches[niches != "not_a_niche"]) > len(niches[niches == "not_a_niche"])
+    for label in niches.unique():
+        if label != "not_a_niche":
+            assert len(niches[niches == label]) >= 100
+
+    rel_nhood_profile, abs_nhood_profile = _calculate_neighborhood_profile(
+        adata_seqfish, groups="celltype_mapped_refined", spatial_connectivities_key=SPATIAL_CONNECTIVITIES_KEY
+    )
+    # assert shape obs x groups
+    assert rel_nhood_profile.shape == (
+        adata_seqfish.n_obs,
+        len(adata_seqfish.obs["celltype_mapped_refined"].cat.categories),
+    )
+    assert abs_nhood_profile.shape == rel_nhood_profile.shape
+    # normalization
+    assert int(rel_nhood_profile.sum(axis=1).sum()) == adata_seqfish.n_obs
+    assert rel_nhood_profile.sum(axis=1).max() == 1
+    # maximum amount of categories equals n_neighbors
+    assert abs_nhood_profile.sum(axis=1).max() == N_NEIGHBORS
+
+
+def test_utag(adata_seqfish: AnnData):
+    """Check whether niche calculation using UTAG approach works as intended."""
+    spatial_neighbors(adata_seqfish, coord_type="generic", delaunay=False, n_neighs=N_NEIGHBORS)
+    calculate_niche(adata_seqfish, flavor="utag", n_neighbors=N_NEIGHBORS, resolutions=[0.1, 1.0])
+
+    niches = adata_seqfish.obs["utag_niche_res=1.0"]
+    niches_low_res = adata_seqfish.obs["utag_niche_res=0.1"]
+
+    assert niches.isna().sum() == 0
+    assert niches.nunique() > niches_low_res.nunique()
+
+    # assert shape obs x var and sparsity in new feature matrix
+    new_feature_matrix = _utag(adata_seqfish, normalize_adj=True, spatial_connectivity_key=SPATIAL_CONNECTIVITIES_KEY)
+    assert new_feature_matrix.shape == adata_seqfish.X.shape
+    assert issparse(new_feature_matrix)
+
+    spatial_neighbors(adata_seqfish, coord_type="generic", delaunay=False, n_neighs=40)
+    new_feature_matrix_more_neighs = _utag(
+        adata_seqfish, normalize_adj=True, spatial_connectivity_key=SPATIAL_CONNECTIVITIES_KEY
+    )
+
+    # matrix products should differ when using different amount of neighbors
+    try:
+        assert_frame_equal(new_feature_matrix, new_feature_matrix_more_neighs)
+    except AssertionError:
+        pass
+    else:
+        raise AssertionError
+
+
+def test_cellcharter_approach(adata_seqfish: AnnData):
+    """Check whether niche calculation using CellCharter approach works as intended."""
+
+    spatial_neighbors(adata_seqfish, coord_type="generic", delaunay=False, n_neighs=N_NEIGHBORS)
+    calculate_niche(
+        adata_seqfish, groups="celltype_mapped_refined", flavor="cellcharter", adj_subsets=3, n_components=5
+    )
+    niches = adata_seqfish.obs["cellcharter_niche"]
+
+    assert niches.nunique() == 5
+    assert niches.isna().sum() == 0
+
+    adj = adata_seqfish.obsp[SPATIAL_CONNECTIVITIES_KEY]
+    adj_hop = _setdiag(adj, value=0)
+    assert adj_hop.shape == adj.shape
+    assert issparse(adj_hop)
+    assert isinstance(adj_hop, scipy.sparse.csrmatrix)
+
+    adj_visited = _setdiag(adj.copy(), 1)  # Track visited neighbors
+    adj_hop, adj_visited = _hop(adj_hop, adj, adj_visited)
+    assert adj_hop.shape == adj.shape
+    assert adj_hop.shape == adj_visited.shape
+
+    assert np.array(np.sum(adj, axis=1)).squeeze() == N_NEIGHBORS
+    adj_hop_norm = _normalize(adj_hop)
+    assert adj_hop_norm.shape == adj.shape
+
+    mean_aggr_matrix = _aggregate(adata_seqfish, adj_hop_norm, "mean")
+    assert mean_aggr_matrix.shape == adata_seqfish.X.shape
+    var_aggr_matrix = _aggregate(adata_seqfish, adj_hop_norm, "variance")
+    assert var_aggr_matrix.shape == adata_seqfish.X.shape
+
+    # TODO: add test for GMM
+
+
+# TODO: comppare results to previously calculated niches