🎨 black

cdlib/algorithms/edge_clustering.py

@@ -1,10 +1,23 @@
 from cdlib import EdgeClustering
 from collections import defaultdict
 import networkx as nx
-from cdlib.utils import convert_graph_formats, nx_node_integer_mapping, remap_edge_communities
-from cdlib.algorithms.internal.HLC import HLC, HLC_read_edge_list_unweighted, HLC_read_edge_list_weighted, HLC_full
-
-__all__ = ["hierarchical_link_community", "hierarchical_link_community_w", "hierarchical_link_community_full"]
+from cdlib.utils import (
+    convert_graph_formats,
+    nx_node_integer_mapping,
+    remap_edge_communities,
+)
+from cdlib.algorithms.internal.HLC import (
+    HLC,
+    HLC_read_edge_list_unweighted,
+    HLC_read_edge_list_weighted,
+    HLC_full,
+)
+
+__all__ = [
+    "hierarchical_link_community",
+    "hierarchical_link_community_w",
+    "hierarchical_link_community_full",
+]
 
 
 def hierarchical_link_community(g_original: object) -> EdgeClustering:
@@ -96,10 +109,18 @@ def hierarchical_link_community_w(g_original: object) -> EdgeClustering:
     return EdgeClustering(coms, g_original, "HLC_w", method_parameters={})
 
 
+def hierarchical_link_community_full(
+    g_original: object,
+    weight="weight",
+    simthr=None,
+    hcmethod="single",
+    min_edges=None,
+    verbose=False,
+) -> EdgeClustering:
     """
     HLC (hierarchical link clustering) is a method to classify links into topologically related groups.
     The algorithm uses a similarity between links to build a dendrogram where each leaf is a link from the original network and branches represent link communities.
-    At each level of the link dendrogram is calculated the partition density function, based on link density inside communities, to pick the best level to cut. 
+    At each level of the link dendrogram is calculated the partition density function, based on link density inside communities, to pick the best level to cut.
     This implementation follows exactly the algorithm described in Ahn et al and uses numpy/scipy to improve the clustering computation (It is faster and consumes less memory.
 
 
@@ -112,11 +133,11 @@ def hierarchical_link_community_w(g_original: object) -> EdgeClustering:
     ========== ======== ========
 
     :param g_original: a networkx/igraph object
-    :weight: None for unweighted networks, jaccard approximation is used. When defined with a string, edge attribute name (usually 'weight') to be used as weight and Tanimoto approximation is used.
-    :simthr: None by default. If set to float, all values less than threshold are set to 0 in similarity matrix (it could reduce memory usage). 
-    :hcmethod: Linkage method used in hierarchical clustering, 'single' by default. See scipy.cluster.hierarchy.linkage to get full method list.
-    :min_edges: None by default. If set to float, minimum number of edges that a community must contain to be kept in the clustering
-    :verbose: If True, write intermediary steps to disk.
+    :param weight: None for unweighted networks, jaccard approximation is used. When defined with a string, edge attribute name (usually 'weight') to be used as weight and Tanimoto approximation is used.
+    :param simthr: None by default. If set to float, all values less than threshold are set to 0 in similarity matrix (it could reduce memory usage).
+    :param hcmethod: Linkage method used in hierarchical clustering, 'single' by default. See scipy.cluster.hierarchy.linkage to get full method list.
+    :param min_edges: None by default. If set to float, minimum number of edges that a community must contain to be kept in the clustering
+    :param verbose: If True, write intermediary steps to disk.
     :return: EdgeClustering object
 
 
@@ -131,11 +152,20 @@ def hierarchical_link_community_w(g_original: object) -> EdgeClustering:
 
     Ahn, Yong-Yeol, James P. Bagrow, and Sune Lehmann. `Link communities reveal multiscale complexity in networks. <https://www.nature.com/articles/nature09182/>`_ nature 466.7307 (2010): 761.
     """
-def hierarchical_link_community_full(g_original: object, weight='weight', simthr=None, hcmethod='single', min_edges= None, verbose=False) -> EdgeClustering:
+
     g = convert_graph_formats(g_original, nx.Graph)
     g_number, dictio = nx_node_integer_mapping(g)
 
-    coms = HLC_full(g_number, weight=weight, simthr=simthr, hcmethod=hcmethod, min_edges=min_edges, verbose=verbose, dictio= dictio).clusters
+    coms = HLC_full(
+        g_number,
+        weight=weight,
+        simthr=simthr,
+        hcmethod=hcmethod,
+        min_edges=min_edges,
+        verbose=verbose,
+        dictio=dictio,
+    ).clusters
     clustering = EdgeClustering(coms, g_number, "HLC_f", method_parameters={})
-    if dictio != None: clustering.communities = remap_edge_communities(clustering.communities, dictio)
-    return clustering
+    if dictio != None:
+        clustering.communities = remap_edge_communities(clustering.communities, dictio)
+    return clustering