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Future-proofing and improve tests #7209

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merged 4 commits into from
Jan 6, 2024
Merged

Future-proofing and improve tests #7209

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26e1a49
Fix intermittent doctest failures for expanders with seeding.
rossbar Jan 5, 2024
cf858a9
Rm deprecated asftype.
rossbar Jan 5, 2024
15a9d54
Fix warnings filter for join.
rossbar Jan 5, 2024
80e6e11
Prepare for pandas copy-on-write.
rossbar Jan 5, 2024
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6 changes: 3 additions & 3 deletions networkx/algorithms/centrality/group.py
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Original file line number Diff line number Diff line change
Expand Up @@ -498,15 +498,15 @@ def _heuristic(k, root, DF_tree, D, nodes, greedy):
/ root_node["sigma"][added_node][y]
)
DF_tree.nodes[node_p]["sigma"][x][y] = root_node["sigma"][x][y] * (1 - dxvy)
DF_tree.nodes[node_p]["betweenness"][x][y] = (
DF_tree.nodes[node_p]["betweenness"].loc[y, x] = (
root_node["betweenness"][x][y] - root_node["betweenness"][x][y] * dxvy
)
if y != added_node:
DF_tree.nodes[node_p]["betweenness"][x][y] -= (
DF_tree.nodes[node_p]["betweenness"].loc[y, x] -= (
root_node["betweenness"][x][added_node] * dxyv
)
if x != added_node:
DF_tree.nodes[node_p]["betweenness"][x][y] -= (
DF_tree.nodes[node_p]["betweenness"].loc[y, x] -= (
root_node["betweenness"][added_node][y] * dvxy
)

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4 changes: 3 additions & 1 deletion networkx/conftest.py
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Expand Up @@ -117,7 +117,9 @@ def set_warnings():
"ignore", category=DeprecationWarning, message="\n\nThe `normalized`"
)
warnings.filterwarnings(
"ignore", category=DeprecationWarning, message="function `join` is deprecated"
"ignore",
category=DeprecationWarning,
message="The function `join` is deprecated",
)
warnings.filterwarnings(
"ignore",
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6 changes: 3 additions & 3 deletions networkx/generators/expanders.py
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Expand Up @@ -248,7 +248,7 @@ def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None)

Examples
--------
>>> G = nx.maybe_regular_expander(n=200, d=6)
>>> G = nx.maybe_regular_expander(n=200, d=6, seed=8020)

Returns
-------
Expand Down Expand Up @@ -387,8 +387,8 @@ def is_regular_expander(G, *, epsilon=0):

_, d = nx.utils.arbitrary_element(G.degree)

A = nx.adjacency_matrix(G)
lams = eigsh(A.asfptype(), which="LM", k=2, return_eigenvectors=False)
A = nx.adjacency_matrix(G, dtype=float)
lams = eigsh(A, which="LM", k=2, return_eigenvectors=False)

# lambda2 is the second biggest eigenvalue
lambda2 = min(lams)
Expand Down