用于NebulaGraph与深度图库(DGL)集成的库。
项目描述
nebula-dgl
nebula-dgl是用于Nebula Graph与深度图库(DGL)集成的库。
nebula-dgl仍处于WIP状态,有一个示例项目在此。
指南
安装
从PyPi安装
python3 -m pip install nebula-dgl
python3 -m pip install dgl dglgo -f https://data.dgl.ai/wheels/repo.html
从代码库安装(开发者版)
python3 -m pip install nebula3-python
python3 -m pip install dgl dglgo -f https://data.dgl.ai/wheels/repo.html
# build and install
python3 -m pip install .
游乐场
首先将此仓库克隆到您的本地目录。
git clone https://github.com/wey-gu/nebula-dgl.git
cd nebula-dgl
- 使用Nebula-UP部署NebulaGraph游乐场
安装NebulaGraph
curl -fsSL nebula-up.siwei.io/install.sh | bash
加载示例数据
~/.nebula-up/load-basketballplayer-dataset.sh
- 在同一docker网络中创建jupyter笔记本:
nebula-net
docker run -it --name dgl -p 8888:8888 --network nebula-net \
-v "$PWD":/home/jovyan/work jupyter/datascience-notebook \
start-notebook.sh --NotebookApp.token='secret'
现在您可以
- 通过http://localhost:8888/lab/tree/work?token=secret访问笔记本并创建一个新的笔记本。
或者
- 在容器中运行ipython
docker exec -it dgl ipython
cd work
- 在笔记本中安装nebula-dgl
安装nebula-dgl
!python3 -m pip install python3 -m pip install nebula3-python==3.3.0
!python3 -m pip install dgl dglgo -f https://data.dgl.ai/wheels/repo.html
!python3 -m pip install .
- 使用同构图尝试
import yaml
import networkx as nx
from nebula_dgl import NebulaLoader
nebula_config = {
"graph_hosts": [
('graphd', 9669),
('graphd1', 9669),
('graphd2', 9669)
],
"nebula_user": "root",
"nebula_password": "nebula",
}
with open('example/homogeneous_graph.yaml', 'r') as f:
feature_mapper = yaml.safe_load(f)
nebula_loader = NebulaLoader(nebula_config, feature_mapper)
homo_dgl_graph = nebula_loader.load()
# or query based
query = """
MATCH p=()-[:follow]->() RETURN p
"""
nebula_loader = NebulaLoader(nebula_config, feature_mapper, query=query, query_space="basketballplayer")
homo_dgl_graph = nebula_loader.load()
nx_g = homo_dgl_graph.to_networkx()
nx.draw(nx_g, with_labels=True, pos=nx.spring_layout(nx_g))
结果
- 计算图的度中心性
nx.degree_centrality(nx_g)
结果
{0: 0.0,
1: 0.04,
2: 0.02,
3: 0.02,
4: 0.06,
5: 0.06,
6: 0.04,
7: 0.24,
8: 0.16,
9: 0.0,
10: 0.02,
11: 0.04,
12: 0.04,
13: 0.04,
14: 0.1,
15: 0.04,
16: 0.0,
17: 0.1,
18: 0.04,
19: 0.04,
20: 0.0,
21: 0.0,
22: 0.04,
23: 0.02,
24: 0.02,
25: 0.04,
26: 0.06,
27: 0.0,
28: 0.02,
29: 0.0,
30: 0.04,
31: 0.12,
32: 0.04,
33: 0.22,
34: 0.14,
35: 0.1,
36: 0.04,
37: 0.14,
38: 0.1,
39: 0.02,
40: 0.14,
41: 0.08,
42: 0.1,
43: 0.12,
44: 0.12,
45: 0.08,
46: 0.1,
47: 0.02,
48: 0.04,
49: 0.12,
50: 0.06}
Nebula Graph到DGL
from nebula_dgl import NebulaLoader
nebula_config = {
"graph_hosts": [
('graphd', 9669),
('graphd1', 9669),
('graphd2', 9669)
],
"nebula_user": "root",
"nebula_password": "nebula"
}
# load feature_mapper from yaml file
with open('example/nebula_to_dgl_mapper.yaml', 'r') as f:
feature_mapper = yaml.safe_load(f)
nebula_loader = NebulaLoader(nebula_config, feature_mapper)
dgl_graph = nebula_loader.load()
在networkx中玩同构图算法
import networkx
with open('example/homogeneous_graph.yaml', 'r') as f:
feature_mapper = yaml.safe_load(f)
nebula_loader = NebulaLoader(nebula_config, feature_mapper)
homo_dgl_graph = nebula_loader.load()
nx_g = homo_dgl_graph.to_networkx()
# plot it
networkx.draw(nx_g, with_lables=True)
# get degree
networkx.degree(nx_g)
# get degree centrality
networkx.degree_centrality(nx_g)
Nebula Graph的多部分加载器
- 目前,多部分加载器的速度像顺序扫描一样慢,需要分析性能。
import yaml
import networkx as nx
import matplotlib.pyplot as plt
from nebula_dgl import NebulaReducedLoader
nebula_config = {
"graph_hosts": [
('127.0.0.1', 9669)
],
"nebula_user": "root",
"nebula_password": "nebula",
}
with open('example/homogeneous_graph.yaml', 'r') as f:
feature_mapper = yaml.safe_load(f)
# you only need change the following line: from NebulaLoader to NebulaReducedLoader
# Easy for you to use the multi-part loader
nebula_reduced_loader = NebulaReducedLoader(nebula_config, feature_mapper)
homo_dgl_graph = nebula_reduced_loader.load()
nx_g = homo_dgl_graph.to_networkx()
nx.draw(nx_g, with_labels=True, pos=nx.spring_layout(nx_g))
plt.savefig("multi_graph.png")
项目详情
下载文件
下载适合您平台的文件。如果您不确定选择哪个,请了解更多关于安装包的信息。
源分发
nebula-dgl-0.1.2.tar.gz (17.1 kB 查看哈希值)
构建分发
nebula_dgl-0.1.2-py3-none-any.whl (18.1 kB 查看哈希值)