fixed clustering for node subset

nngt/analysis/clustering.py

@@ -24,6 +24,7 @@
 import numpy as np
 
 import nngt
+from nngt.lib import nonstring_container
 
 
 __all__ = [
@@ -295,22 +296,23 @@ def local_clustering(g, nodes=None, directed=True, weights=None,
     directed *= g.is_directed()
     weighted  = weights not in (None, False)
 
+    triplets, triangles = None, None
+
     if not directed and not weighted:
         # undirected binary clustering uses the library method
         return local_clustering_binary_undirected(g, nodes=nodes)
     elif not weighted:
         # directed clustering
         triangles = triangle_count(g, nodes=nodes, mode=mode)
         triplets  = triplet_count(g, nodes, mode=mode).astype(float)
+    else:
+        triangles, triplets = _triangles_and_triplets(
+            g, directed, weights, method, mode, combine_weights, nodes)
 
+    if nonstring_container(triplets):
         triplets[triangles == 0] = 1
-
-        return triangles / triplets
-
-    triangles, triplets = _triangles_and_triplets(
-        g, directed, weights, method, mode, combine_weights, nodes)
-
-    triplets[triangles == 0] = 1
+    elif triangles == 0:
+        return 0
 
     return triangles / triplets
 

testing/test_analysis.py

@@ -325,12 +325,25 @@ def test_clustering_parameters():
     cc_bu = na.local_clustering_binary_undirected(g)
 
     for m in methods:
+        # combine
         for combine in ("sum", "max", "min", "mean"):
             cc_wu = na.local_clustering(g, directed=False, weights=True,
                                         combine_weights=combine, method=m)
 
             assert np.all(np.isclose(cc_wu, cc_bu))
 
+        # subset of nodes
+        nodes = [1, [1, 2]]
+
+        for n in nodes:
+            cc = na.local_clustering_binary_undirected(g, nodes=n)
+
+            assert np.all(np.isclose(cc, cc_bu[n]))
+
+            cc = na.local_clustering(g, nodes=n, method=m)
+
+            assert np.all(np.isclose(cc, cc_bu[n]))
+
     # check different but equivalent matrices
     for m in methods:
         W = np.array([
@@ -358,6 +371,16 @@ def test_clustering_parameters():
 
         assert np.all(np.isclose(cc_sum, cc_sym))
 
+        # subset of nodes
+        nodes = [1, [1, 2]]
+
+        cc_dir = na.local_clustering(g, weights="weight", method=m)
+
+        for n in nodes:
+            cc = na.local_clustering(g, nodes=n, weights="weight", method=m)
+
+            assert np.all(np.isclose(cc, cc_dir[n]))
+
 
 @pytest.mark.mpi_skip
 def test_partial_directed_clustering():
@@ -610,11 +633,11 @@ def test_swp():
 
 if __name__ == "__main__":
     if not nngt.get_config("mpi"):
-        test_binary_undirected_clustering()
-        test_weighted_undirected_clustering()
-        test_weighted_directed_clustering()
-        test_reciprocity()
-        test_iedges()
-        test_swp()
-        test_partial_directed_clustering()
+        # ~ test_binary_undirected_clustering()
+        # ~ test_weighted_undirected_clustering()
+        # ~ test_weighted_directed_clustering()
+        # ~ test_reciprocity()
+        # ~ test_iedges()
+        # ~ test_swp()
+        # ~ test_partial_directed_clustering()
         test_clustering_parameters()