DGL学习(一)：使用DGL跑一个最简单的GCN

使用没有节点特征的图来跑DGL (输入特征为节点编号的embedding)

安装DGL ：

pip install dgl

所需要的包

import dgl
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F

构建无向图:

def build_karate_club_graph():
    # All 78 edges are stored in two numpy arrays. One for source endpoints
    # while the other for destination endpoints.
    src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
                    10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
                    25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
                    32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
                    33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
    dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
                    5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
                    24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
                    29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
                    31, 32])
    # Edges are directional in DGL; Make them bi-directional.
    u = np.concatenate([src, dst])
    v = np.concatenate([dst, src])
    # Construct a DGLGraph
    return dgl.DGLGraph((u, v))

G = build_karate_club_graph()
print("G中节点数 %d."% G.number_of_nodes()) # 34
print("G中边数 %d."% G.number_of_edges()) # 156

转为networkX进行可视化

def visual(G):
    # 可视化
    nx_G = G.to_networkx().to_undirected()
    pos = nx.kamada_kawai_layout(nx_G) ## 生成节点位置
    nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])
    plt.pause(10)

 对每个节点做embedding并作为GCN的输入特征:

## 对 34 个节点做embedding
embed = nn.Embedding(34, 5)  # 34 nodes with embedding dim equal to 5
print(embed.weight)
G.ndata['feat'] = embed.weight

训练GCN：

def train(G, inputs, embed, labeled_nodes,labels):
    net = GCN(5,5,2)
    import itertools

    optimizer = torch.optim.Adam(itertools.chain(net.parameters(), embed.parameters()), lr=0.01)
    all_logits = []
    for epoch in range(50):
        logits = net(G, inputs)
        # we save the logits for visualization later
        all_logits.append(logits.detach()) # detach代表从当前计算图中分离下来的
        logp = F.log_softmax(logits, 1)
        # 半监督学习， 只使用标记的节点计算loss
        loss = F.nll_loss(logp[labeled_nodes], labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print('Epoch %d | Loss: %.4f' % (epoch, loss.item()))


    print(all_logits)

train(G, embed.weight, embed, torch.tensor([0,33]), torch.tensor([0,1]))

Epoch 0 | Loss: 0.9247
Epoch 1 | Loss: 0.8673
Epoch 2 | Loss: 0.8160
Epoch 3 | Loss: 0.7713
Epoch 4 | Loss: 0.7328
Epoch 5 | Loss: 0.6999
Epoch 6 | Loss: 0.6748
Epoch 7 | Loss: 0.6551
Epoch 8 | Loss: 0.6392
Epoch 9 | Loss: 0.6252
Epoch 10 | Loss: 0.6120
Epoch 11 | Loss: 0.5989
Epoch 12 | Loss: 0.5854
Epoch 13 | Loss: 0.5713
Epoch 14 | Loss: 0.5559
Epoch 15 | Loss: 0.5391
Epoch 16 | Loss: 0.5210
Epoch 17 | Loss: 0.5031
Epoch 18 | Loss: 0.4867
Epoch 19 | Loss: 0.4696
Epoch 20 | Loss: 0.4522
Epoch 21 | Loss: 0.4347
Epoch 22 | Loss: 0.4168
Epoch 23 | Loss: 0.3987
Epoch 24 | Loss: 0.3808
Epoch 25 | Loss: 0.3627
Epoch 26 | Loss: 0.3448
Epoch 27 | Loss: 0.3269
Epoch 28 | Loss: 0.3090
Epoch 29 | Loss: 0.2913
Epoch 30 | Loss: 0.2738
Epoch 31 | Loss: 0.2566
Epoch 32 | Loss: 0.2396
Epoch 33 | Loss: 0.2230
Epoch 34 | Loss: 0.2069
Epoch 35 | Loss: 0.1913
Epoch 36 | Loss: 0.1762
Epoch 37 | Loss: 0.1618
Epoch 38 | Loss: 0.1479
Epoch 39 | Loss: 0.1347
Epoch 40 | Loss: 0.1224
Epoch 41 | Loss: 0.1111
Epoch 42 | Loss: 0.1007
Epoch 43 | Loss: 0.0910
Epoch 44 | Loss: 0.0822
Epoch 45 | Loss: 0.0742
Epoch 46 | Loss: 0.0670
Epoch 47 | Loss: 0.0605
Epoch 48 | Loss: 0.0546
Epoch 49 | Loss: 0.0494

对每轮的分类结果进行可视化

def draw(i):
    cls1color = '#00FFFF'
    cls2color = '#FF00FF'
    pos = {}
    colors = []
    for v in range(34):
        pos[v] = all_logits[i][v].numpy()
        cls = pos[v].argmax()
        colors.append(cls1color if cls else cls2color)
    ax.cla()
    ax.axis('off')
    ax.set_title('Epoch: %d' % i)
    nx.draw_networkx(nx_G.to_undirected(), pos, node_color=colors,
                     with_labels=True, node_size=300, ax=ax)
nx_G = G.to_networkx().to_undirected()
fig = plt.figure(dpi=150)
fig.clf()
ax = fig.subplots()
for i in range(50):
    draw(i)
    plt.pause(0.2)

plt.show()

完整代码:

import dgl
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F

def build_karate_club_graph():
    # All 78 edges are stored in two numpy arrays. One for source endpoints
    # while the other for destination endpoints.
    src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
                    10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
                    25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
                    32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
                    33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
    dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
                    5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
                    24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
                    29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
                    31, 32])
    # Edges are directional in DGL; Make them bi-directional.
    u = np.concatenate([src, dst])
    v = np.concatenate([dst, src])
    # Construct a DGLGraph
    return dgl.DGLGraph((u, v))

def visual(G):
    # 可视化
    nx_G = G.to_networkx().to_undirected()
    pos = nx.kamada_kawai_layout(nx_G) ## 生成节点位置
    nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])
    plt.pause(10)

from dgl.nn.pytorch import GraphConv
class GCN(nn.Module):
    def __init__(self, in_feats, hidden_size, num_classes):
        super(GCN, self).__init__()
        self.conv1 = GraphConv(in_feats, hidden_size)
        self.conv2 = GraphConv(hidden_size, num_classes)

    def forward(self, g, inputs):
        h = self.conv1(g, inputs)
        h = torch.relu(h)
        h = self.conv2(g, h)
        return h

def train(G, inputs, embed, labeled_nodes,labels):
    net = GCN(5,5,2)
    import itertools

    optimizer = torch.optim.Adam(itertools.chain(net.parameters(), embed.parameters()), lr=0.01)
    all_logits = []
    for epoch in range(50):
        logits = net(G, inputs)
        # we save the logits for visualization later
        all_logits.append(logits.detach()) # detach代表从当前计算图中分离下来的
        logp = F.log_softmax(logits, 1)
        # 半监督学习， 只使用标记的节点计算loss
        loss = F.nll_loss(logp[labeled_nodes], labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print('Epoch %d | Loss: %.4f' % (epoch, loss.item()))


    print(all_logits)


    def draw(i):
        cls1color = '#00FFFF'
        cls2color = '#FF00FF'
        pos = {}
        colors = []
        for v in range(34):
            pos[v] = all_logits[i][v].numpy()
            cls = pos[v].argmax()
            colors.append(cls1color if cls else cls2color)
        ax.cla()
        ax.axis('off')
        ax.set_title('Epoch: %d' % i)
        nx.draw_networkx(nx_G.to_undirected(), pos, node_color=colors,
                         with_labels=True, node_size=300, ax=ax)
    nx_G = G.to_networkx().to_undirected()
    fig = plt.figure(dpi=150)
    fig.clf()
    ax = fig.subplots()
    for i in range(50):
        draw(i)
        plt.pause(0.2)

    plt.show()
def main():
    G = build_karate_club_graph()
    print("G中节点数 %d."% G.number_of_nodes())
    print("G中边数 %d."% G.number_of_edges())

    visual(G)

    ## 对 34 个节点做embedding
    embed = nn.Embedding(34, 5)  # 34 nodes with embedding dim equal to 5
    print(embed.weight)
    G.ndata['feat'] = embed.weight

    # print out node 2's input feature
    print(G.ndata['feat'][2])
    # print out node 10 and 11's input features
    print(G.ndata['feat'][[10, 11]])

    train(G, embed.weight, embed, torch.tensor([0,33]), torch.tensor([0,1]))


main()