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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import scipy as sp
import sklearn as sk
import time
import gc%matplotlib inline
%config InlineBackend.figure_format = 'retina'import warnings
warnings.filterwarnings("ignore")
plt.rcParams['figure.figsize'] = (10.0, 8.0)
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', None)
pd.set_option('display.min_rows', 14)
pd.set_option('display.max_colwidth',100)
# pd.reset_option('display.max_columns')

import dgl
import torch
import torch.nn as nn
import torch.nn.functional as Ffrom importlib import reload

Using backend: pytorch

导入数据集

import dgl.datadataSet = dgl.data.CoraGraphDataset()

Using backend: pytorchLoading from cache failed, re-processing.
Finished data loading and preprocessing.NumNodes: 2708NumEdges: 10556NumFeats: 1433NumClasses: 7NumTrainingSamples: 140NumValidationSamples: 500NumTestSamples: 1000
Done saving data into cached files.

访问数据集 ndata edata

# 数据集名称
dataSet.name

'cora_v2'

# 查看有哪些图
dataSet.graph

<networkx.classes.digraph.DiGraph at 0x7fdf0458e290>

# 查看label个数
dataSet.num_classes

7

# 一个数据集可能有多个graph 通过下标访问graph
dataSet[0]

Graph(num_nodes=2708, num_edges=10556,ndata_schemes={'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool), 'label': Scheme(shape=(), dtype=torch.int64), 'feat': Scheme(shape=(1433,), dtype=torch.float32)}edata_schemes={})

节点信息

• 为什么训练集 验证集 测试集的节点数量之和不等于节点总数？

dataSet[0].ndata.keys()

dict_keys(['train_mask', 'val_mask', 'test_mask', 'label', 'feat'])

# 训练集 mask
dataSet[0].ndata['train_mask']

tensor([ True,  True,  True,  ..., False, False, False])

# 训练集包含140个节点
dataSet[0].ndata['train_mask'].sum()

tensor(140)

# 验证集包含500个节点
dataSet[0].ndata['val_mask'].sum()

tensor(500)

# 测试集
dataSet[0].ndata['test_mask']

tensor([False, False, False,  ...,  True,  True,  True])

# 所有节点的分类
dataSet[0].ndata['label']

tensor([3, 4, 4,  ..., 3, 3, 3])

# 所有节点的features
dataSet[0].ndata['feat']

tensor([[0., 0., 0.,  ..., 0., 0., 0.],[0., 0., 0.,  ..., 0., 0., 0.],[0., 0., 0.,  ..., 0., 0., 0.],...,[0., 0., 0.,  ..., 0., 0., 0.],[0., 0., 0.,  ..., 0., 0., 0.],[0., 0., 0.,  ..., 0., 0., 0.]])

边信息

dataSet[0].edata

{}

构造模型并训练

• 见dgl_1_gcn.py

构造dgl graph并查看图信息

# src_ids = [0, 0, 0]
src_ids = torch.tensor([0, 0, 0])
# dest_ids = [1, 2, 3]
dest_ids = torch.tensor([1, 2, 3])

graph = dgl.graph((src_ids, dest_ids), num_nodes = 4)'''
dgl.graph params:data: (src nodes id, dest nodes id) 一一对应
num_nodes: 节点个数构造有向图，边直接由起始节点和终止节点决定，边的id由定义顺序决定
'''

'\ndgl.graph params:\n\ndata: (src nodes id, dest nodes id) 一一对应\nnum_nodes: 节点个数\n\n构造有向图，边直接由起始节点和终止节点决定，边的id由定义顺序决定\n'

# 查看边
graph.edges()

(tensor([0, 0, 0]), tensor([1, 2, 3]))

graph.num_edges()

3

# 给定起始和终止节点查看边id
graph.edge_id(2, 0)

---------------------------------------------------------------------------DGLError                                  Traceback (most recent call last)<ipython-input-23-71bec222622a> in <module>1 # 给定起始和终止节点查看边id
----> 2 graph.edge_id(2, 0)~/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/dgl/heterograph.py in edge_id(self, u, v, force_multi, return_uv, etype)2713         dgl_warning("DGLGraph.edge_id is deprecated. Please use DGLGraph.edge_ids.")2714         return self.edge_ids(u, v, force_multi=force_multi,
-> 2715                              return_uv=return_uv, etype=etype)2716 2717     def edge_ids(self, u, v, force_multi=None, return_uv=False, etype=None):~/anaconda3/envs/DeepLearning/lib/python3.7/site-packages/dgl/heterograph.py in edge_ids(self, u, v, force_multi, return_uv, etype)2835                 raise DGLError("Error: (%d, %d) does not form a valid edge." % (2836                     F.as_scalar(F.gather_row(u, idx)),
-> 2837                     F.as_scalar(F.gather_row(v, idx))))2838             return F.as_scalar(eid) if is_int else eid2839 DGLError: Error: (2, 0) does not form a valid edge.

graph.edge_id(0, 2)

1

# 查看点
graph.nodes()

tensor([0, 1, 2, 3])

graph.num_nodes()

4

# 全图出度之和
graph.num_src_nodes()

4

# 全图入度之和
graph.num_dst_nodes()

4

# 某个点的出度
graph.out_degrees(1)

0

# 某个点的入度
graph.in_degrees(1)

1

# 邻接矩阵
graph.adj()

tensor(indices=tensor([[0, 0, 0],[1, 2, 3]]),values=tensor([1., 1., 1.]),size=(4, 4), nnz=3, layout=torch.sparse_coo)

graph.adjacency_matrix_scipy()

<4x4 sparse matrix of type '<class 'numpy.int64'>'with 3 stored elements in Compressed Sparse Row format>

g, _= dgl.load_graphs('./data/graph.bin', [0])
g = g[0]
g

Graph(num_nodes=4, num_edges=3,ndata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float32)}edata_schemes={'feat': Scheme(shape=(4,), dtype=torch.float32)})

g.srcdata

{'feat': tensor([[ 1.0556,  0.1928,  1.0018],[ 2.2849, -1.3030,  1.3342],[ 1.8428,  0.2911, -0.0281],[-0.3295,  0.4281, -1.9331]])}

g.srcdata.update({'a': torch.tensor([1, 2, 3, 4])})
# g.srcdata['a'] = torch.tensor([1, 2, 3, 4])

g.srcdata

{'feat': tensor([[ 1.0556,  0.1928,  1.0018],[ 2.2849, -1.3030,  1.3342],[ 1.8428,