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import os.path as osp
import sys
import matplotlib.pyplot as plt
import torch
from sklearn.manifold import TSNE
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import Node2Vec
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
dataset = Planetoid(path, name='Cora')
data = dataset[0]
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = Node2Vec(
data.edge_index,
embedding_dim=128,
walk_length=20,
context_size=10,
walks_per_node=10,
num_negative_samples=1,
p=1.0,
q=1.0,
sparse=True,
).to(device)
num_workers = 4 if sys.platform == 'linux' else 0
loader = model.loader(batch_size=128, shuffle=True, num_workers=num_workers)
optimizer = torch.optim.SparseAdam(list(model.parameters()), lr=0.01)
def train():
model.train()
total_loss = 0
for pos_rw, neg_rw in loader:
optimizer.zero_grad()
loss = model.loss(pos_rw.to(device), neg_rw.to(device))
loss.backward()
optimizer.step()
total_loss += loss.item()
return total_loss / len(loader)
@torch.no_grad()
def test():
model.eval()
z = model()
acc = model.test(
train_z=z[data.train_mask],
train_y=data.y[data.train_mask],
test_z=z[data.test_mask],
test_y=data.y[data.test_mask],
max_iter=150,
)
return acc
for epoch in range(1, 101):
loss = train()
acc = test()
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Acc: {acc:.4f}')
@torch.no_grad()
def plot_points(colors):
model.eval()
z = model().cpu().numpy()
z = TSNE(n_components=2).fit_transform(z)
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(colors)
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