1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
|
import os.path as osp
import matplotlib.pyplot as plt
import torch
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
from sklearn.metrics.cluster import (
completeness_score,
homogeneity_score,
v_measure_score,
)
from torch.nn import Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import ARGVA, GCNConv
if torch.cuda.is_available():
device = torch.device('cuda')
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
device = torch.device('mps')
else:
device = torch.device('cpu')
transform = T.Compose([
T.ToDevice(device),
T.RandomLinkSplit(num_val=0.05, num_test=0.1, is_undirected=True,
split_labels=True, add_negative_train_samples=False),
])
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
dataset = Planetoid(path, name='Cora', transform=transform)
train_data, val_data, test_data = dataset[0]
class Encoder(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv_mu = GCNConv(hidden_channels, out_channels)
self.conv_logstd = GCNConv(hidden_channels, out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
return self.conv_mu(x, edge_index), self.conv_logstd(x, edge_index)
class Discriminator(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.lin1 = Linear(in_channels, hidden_channels)
self.lin2 = Linear(hidden_channels, hidden_channels)
self.lin3 = Linear(hidden_channels, out_channels)
def forward(self, x):
x = self.lin1(x).relu()
x = self.lin2(x).relu()
return self.lin3(x)
encoder = Encoder(train_data.num_features, hidden_channels=32, out_channels=32)
discriminator = Discriminator(in_channels=32, hidden_channels=64,
out_channels=32)
model = ARGVA(encoder, discriminator).to(device)
encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=0.005)
discriminator_optimizer = torch.optim.Adam(discriminator.parameters(),
lr=0.001)
def train():
model.train()
encoder_optimizer.zero_grad()
z = model.encode(train_data.x, train_data.edge_index)
# We optimize the discriminator more frequently than the encoder.
for i in range(5):
discriminator_optimizer.zero_grad()
discriminator_loss = model.discriminator_loss(z)
discriminator_loss.backward()
discriminator_optimizer.step()
loss = model.recon_loss(z, train_data.pos_edge_label_index)
loss = loss + model.reg_loss(z)
loss = loss + (1 / train_data.num_nodes) * model.kl_loss()
loss.backward()
encoder_optimizer.step()
return float(loss)
@torch.no_grad()
def test(data):
model.eval()
z = model.encode(data.x, data.edge_index)
# Cluster embedded values using k-means.
kmeans_input = z.cpu().numpy()
kmeans = KMeans(n_clusters=7, random_state=0,
n_init='auto').fit(kmeans_input)
pred = kmeans.predict(kmeans_input)
labels = data.y.cpu().numpy()
completeness = completeness_score(labels, pred)
hm = homogeneity_score(labels, pred)
nmi = v_measure_score(labels, pred)
auc, ap = model.test(z, data.pos_edge_label_index,
data.neg_edge_label_index)
return auc, ap, completeness, hm, nmi
for epoch in range(1, 151):
loss = train()
auc, ap, completeness, hm, nmi = test(test_data)
print(f'Epoch: {epoch:03d}, Loss: {loss:.3f}, AUC: {auc:.3f}, '
f'AP: {ap:.3f}, Completeness: {completeness:.3f}, '
f'Homogeneity: {hm:.3f}, NMI: {nmi:.3f}')
@torch.no_grad()
def plot_points(data, colors):
model.eval()
z = model.encode(data.x, data.edge_index)
z = TSNE(n_components=2).fit_transform(z.cpu().numpy())
y = data.y.cpu().numpy()
plt.figure(figsize=(8, 8))
for i in range(dataset.num_classes):
plt.scatter(z[y == i, 0], z[y == i, 1], s=20, color=colors[i])
plt.axis('off')
plt.show()
colors = [
'#ffc0cb', '#bada55', '#008080', '#420420', '#7fe5f0', '#065535', '#ffd700'
]
plot_points(test_data, colors)
|
