[英]Multiplying and adding weights networkx graph python
之前,我曾問過一個有關在networkx中乘以權重以找到有向圖中節點總數的問題。 如果兩個節點之間只有一條路徑,則提供的解決方案會很好,但如果路徑多,則失敗。 一個簡單的例子:
import pandas as pd
data = pd.DataFrame({'shop': ['S1', 'S1', 'S2', 'S2', 'S3'],
'owner': ['S2', 'S3', 'O1', 'O2', 'O1'],
'share': [0.8, 0.2, 0.5, 0.5, 1.0]})
owner shop share
0 S2 S1 0.8
1 S3 S1 0.2
2 O1 S2 0.5
3 O2 S2 0.5
4 O1 S3 1.0
創建圖:
import networkx as nx
G = nx.from_pandas_edgelist(data,'shop','owner',edge_attr = ('share'),
create_using=nx.DiGraph())
pos=nx.spring_layout(G, k = 0.5, iterations = 20)
node_labels = {node:node for node in G.nodes()}
nx.draw_networkx(G, pos, labels = node_labels, arrowstyle = '-|>',
arrowsize = 20, font_size = 15, font_weight = 'bold')
為了獲得O1在S1中的份額,需要將2條路徑相乘然后相加。 以前的解決方案無法做到這一點。 有辦法嗎?
您可以通過以下方式修改以前的解決方案:
from operator import mul
import pandas as pd
import networkx as nx
from functools import reduce
data = pd.DataFrame({'shop': ['S1', 'S1', 'S2', 'S2', 'S3'],
'owner': ['S2', 'S3', 'O1', 'O2', 'O1'],
'share': [0.8, 0.2, 0.5, 0.5, 1.0]})
G = nx.from_pandas_edgelist(data,'shop','owner',edge_attr = ('share'),
create_using=nx.DiGraph())
owners = set(data['owner'])
shops = set(data['shop'])
result = []
summary = {}
for shop in shops:
for owner in owners:
for path in nx.all_simple_paths(G, shop, owner):
share = reduce(mul, (G[start][end]['share'] for start, end in zip(path[:-1], path[1:])), 1)
summary[(owner, shop)] = summary.get((owner, shop), 0) + share
summary = pd.DataFrame.from_dict(summary, orient = 'index', columns = 'share'.split())
print(summary)
產量
share
(O2, S2) 0.5
(O2, S1) 0.4
(S3, S1) 0.2
(O1, S2) 0.5
(O1, S3) 1.0
(O1, S1) 0.6
(S2, S1) 0.8
該行:
share = reduce(mul, (G[start][end]['share'] for start, end in zip(path[:-1], path[1:])), 1)
計算特定路徑的份額。 然后,使用下一行在所有路徑上匯總此份額:
summary[(owner, shop)] = summary.get((owner, shop), 0) + share
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