diff --git a/netbone/structural/high_salience_skeleton.py b/netbone/structural/high_salience_skeleton.py
index aed9f976f03ca615833b6344681810b575f308c7..77bb084dd05de67097f142b660fad80ce2aadccc 100644
--- a/netbone/structural/high_salience_skeleton.py
+++ b/netbone/structural/high_salience_skeleton.py
@@ -6,164 +6,35 @@ import warnings
from netbone.backbone import Backbone
from netbone.filters import boolean_filter, threshold_filter, fraction_filter
-# algo: high_salience_skeleton.py
-warnings.filterwarnings('ignore')
-
+# change distance
def high_salience_skeleton(data):
- # sys.stderr.write("Calculating HSS score...\n")
- undirected=True
- return_self_loops=False
-
+ graph = data.copy()
if isinstance(data, pd.DataFrame):
- table = data.copy()
- elif isinstance(data, nx.Graph):
- table = nx.to_pandas_edgelist(data)
+ graph = nx.from_pandas_edgelist(data)
else:
print("data should be a panads dataframe or nx graph")
return
-
- table["distance"] = 1.0 / table["weight"]
- nodes = set(table["source"]) | set(table["target"])
- G = nx.from_pandas_edgelist(
- table, source="source", target="target", edge_attr="distance", create_using=nx.DiGraph())
- cs = defaultdict(float)
- for s in nodes:
- pred = defaultdict(list)
- dist = {t: float("inf") for t in nodes}
- dist[s] = 0.0
- Q = defaultdict(list)
- for w in dist:
- Q[dist[w]].append(w)
- S = []
- while len(Q) > 0:
- v = Q[min(Q.keys())].pop(0)
- S.append(v)
- for _, w, l in G.edges(nbunch=[v, ], data=True):
- new_distance = dist[v] + l["distance"]
- if dist[w] > new_distance:
- Q[dist[w]].remove(w)
- dist[w] = new_distance
- Q[dist[w]].append(w)
- pred[w] = []
- if dist[w] == new_distance:
- pred[w].append(v)
- while len(S) > 0:
- w = S.pop()
- for v in pred[w]:
- cs[(v, w)] += 1.0
- Q = defaultdict(list, {k: v for k, v in Q.items() if len(v) > 0})
- table["score"] = table.apply(
- lambda x: cs[(x["source"], x["target"])] / len(nodes), axis=1)
- if not return_self_loops:
- table = table[table["source"] != table["target"]]
- if undirected:
- table["edge"] = table.apply(
- lambda x: "%s-%s" % (min(x["source"], x["target"]), max(x["source"], x["target"])), axis=1)
- table_maxscore = table.groupby(by="edge")["score"].sum().reset_index()
- table = table.merge(table_maxscore, on="edge", suffixes=("_min", ""))
- table = table.drop_duplicates(subset=["edge"])
- table = table.drop("edge", 1)
- table = table.drop("score_min", 1)
- G = nx.from_pandas_edgelist(table, edge_attr=['weight', 'score'])
- for u,v in G.edges():
- if G[u][v]['score']>=0.8:
- G[u][v]['high_salience_skeleton'] = True
- else:
- G[u][v]['high_salience_skeleton'] = False
+ wes= nx.get_edge_attributes(graph, 'weight')
+ values = {pair:1/wes[pair] for pair in wes}
+ nx.set_edge_attributes(graph, values, name='distance')
+
+ nx.set_edge_attributes(graph, 0, name='score')
+
+ for source in graph.nodes():
+ tree = nx.single_source_dijkstra(graph, source, cutoff=None, weight='distance')[1]
+ node_tree_scores = dict()
+
+ paths = list(tree.values())[1:]
+ for path in paths:
+ for i in range(len(path) - 1):
+ node_tree_scores[(path[i], path[i+1])] = 1
- return Backbone(G, name="High Salience Skeleton Filter", column="high_salience_skeleton", ascending=False, filters=[boolean_filter, threshold_filter, fraction_filter])
- # return table[["source", "target", "weight", "score"]], "high_salience_skeleton"
+ for u,v in node_tree_scores:
+ graph[u][v]['score'] +=1
-#
-#
-# from collections import defaultdict
-# import networkx as nx
-# import pandas as pd
-# import warnings
-# from netbone.backbone import Backbone
-# from netbone.filters import boolean_filter, threshold_filter, fraction_filter
-#
-# # algo: high_salience_skeleton.py
-#
-# def high_salience_skeleton(data):
-# graph = data.copy()
-# if isinstance(data, pd.DataFrame):
-# data = nx.from_pandas_edgelist(data)
-# else:
-# print("data should be a panads dataframe or nx graph")
-# return
-#
-# wes= nx.get_edge_attributes(graph, 'weight')
-# values = {pair:1/wes[pair] for pair in wes}
-# nx.set_edge_attributes(graph, values, name='distance')
-#
-# nx.set_edge_attributes(graph, 0, name='score')
-# for source in graph.nodes():
-# tree = nx.single_source_dijkstra(graph, source, cutoff=None, weight='distance')[1]
-# paths = list(tree.values())[1:]
-# for path in paths:
-# pairs = [(path[i], path[i+1]) for i in range(len(path) - 1)]
-# for u,v in pairs:
-# graph[u][v]['score'] +=1
-#
-# scores= nx.get_edge_attributes(graph, 'score')
-# N = len(graph)
-# values = {pair:scores[pair]/N for pair in scores}
-# nx.set_edge_attributes(graph, values, name='score')
-# return Backbone(graph, name="High Salience Skeleton Filter", column="high_salience_skeleton", ascending=False, filters=[boolean_filter, threshold_filter, fraction_filter])
-#
-# # table["distance"] = 1.0 / table["weight"]
-# # nodes = set(table["source"]) | set(table["target"])
-# # G = nx.from_pandas_edgelist(
-# # table, source="source", target="target", edge_attr="distance", create_using=nx.DiGraph())
-# # cs = defaultdict(float)
-# # for s in nodes:
-# # pred = defaultdict(list)
-# # dist = {t: float("inf") for t in nodes}
-# # dist[s] = 0.0
-# # Q = defaultdict(list)
-# # for w in dist:
-# # Q[dist[w]].append(w)
-# # S = []
-# # while len(Q) > 0:
-# # v = Q[min(Q.keys())].pop(0)
-# # S.append(v)
-# # for _, w, l in G.edges(nbunch=[v, ], data=True):
-# # new_distance = dist[v] + l["distance"]
-# # if dist[w] > new_distance:
-# # Q[dist[w]].remove(w)
-# # dist[w] = new_distance
-# # Q[dist[w]].append(w)
-# # pred[w] = []
-# # if dist[w] == new_distance:
-# # pred[w].append(v)
-# # while len(S) > 0:
-# # w = S.pop()
-# # for v in pred[w]:
-# # cs[(v, w)] += 1.0
-# # Q = defaultdict(list, {k: v for k, v in Q.items() if len(v) > 0})
-# # table["score"] = table.apply(
-# # lambda x: cs[(x["source"], x["target"])] / len(nodes), axis=1)
-# # if not return_self_loops:
-# # table = table[table["source"] != table["target"]]
-# # if undirected:
-# # table["edge"] = table.apply(
-# # lambda x: "%s-%s" % (min(x["source"], x["target"]), max(x["source"], x["target"])), axis=1)
-# # table_maxscore = table.groupby(by="edge")["score"].sum().reset_index()
-# # table = table.merge(table_maxscore, on="edge", suffixes=("_min", ""))
-# # table = table.drop_duplicates(subset=["edge"])
-# # table = table.drop("edge", 1)
-# # table = table.drop("score_min", 1)
-# #
-# # G = nx.from_pandas_edgelist(table, edge_attr=['weight', 'score'])
-# # for u,v in G.edges():
-# # if G[u][v]['score']>=0.8:
-# # G[u][v]['high_salience_skeleton'] = True
-# # else:
-# # G[u][v]['high_salience_skeleton'] = False
-# #
-# # return Backbone(G, name="High Salience Skeleton Filter", column="high_salience_skeleton", ascending=False, filters=[boolean_filter, threshold_filter, fraction_filter])
-# # # return table[["source", "target", "weight", "score"]], "high_salience_skeleton"
-#
-#
+ scores= nx.get_edge_attributes(graph, 'score')
+ N = len(graph)
+ values = {pair:scores[pair]/N for pair in scores}
+ nx.set_edge_attributes(graph, values, name='score')
+ return Backbone(graph, name="High Salience Skeleton Filter", column="high_salience_skeleton", ascending=False, filters=[boolean_filter, threshold_filter, fraction_filter])