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import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import MovieLens
from torch_geometric.loader import LinkNeighborLoader
from torch_geometric.nn import SAGEConv, to_hetero
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens')
dataset = MovieLens(path, model_name='all-MiniLM-L6-v2')
data = dataset[0]
# Add user node features for message passing:
data['user'].x = torch.eye(data['user'].num_nodes, device=device)
del data['user'].num_nodes
# Add a reverse ('movie', 'rev_rates', 'user') relation for message passing:
data = T.ToUndirected()(data)
del data['movie', 'rev_rates', 'user'].edge_label # Remove "reverse" label.
# Perform a 80/10/10 temporal link-level split:
perm = torch.argsort(data['user', 'movie'].time)
train_idx = perm[:int(0.8 * perm.size(0))]
val_idx = perm[int(0.8 * perm.size(0)):int(0.9 * perm.size(0))]
test_idx = perm[int(0.9 * perm.size(0)):]
edge_index = data['user', 'movie'].edge_index
kwargs = dict(
data=data,
num_neighbors=[20, 10],
batch_size=1024,
time_attr='time',
temporal_strategy='last',
num_workers=4,
persistent_workers=True,
)
train_loader = LinkNeighborLoader(
edge_label_index=(('user', 'movie'), edge_index[:, train_idx]),
edge_label=data['user', 'movie'].edge_label[train_idx],
edge_label_time=data['user', 'movie'].time[train_idx] - 1,
shuffle=True,
**kwargs,
)
val_loader = LinkNeighborLoader(
edge_label_index=(('user', 'movie'), edge_index[:, val_idx]),
edge_label=data['user', 'movie'].edge_label[val_idx],
edge_label_time=data['user', 'movie'].time[val_idx] - 1,
**kwargs,
)
test_loader = LinkNeighborLoader(
edge_label_index=(('user', 'movie'), edge_index[:, test_idx]),
edge_label=data['user', 'movie'].edge_label[test_idx],
edge_label_time=data['user', 'movie'].time[test_idx] - 1,
**kwargs,
)
class GNNEncoder(torch.nn.Module):
def __init__(self, hidden_channels, out_channels):
super().__init__()
self.conv1 = SAGEConv((-1, -1), hidden_channels)
self.conv2 = SAGEConv((-1, -1), out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index)
return x
class EdgeDecoder(torch.nn.Module):
def __init__(self, hidden_channels):
super().__init__()
self.lin1 = Linear(2 * hidden_channels, hidden_channels)
self.lin2 = Linear(hidden_channels, 1)
def forward(self, z_dict, edge_label_index):
row, col = edge_label_index
z = torch.cat([z_dict['user'][row], z_dict['movie'][col]], dim=-1)
z = self.lin1(z).relu()
z = self.lin2(z)
return z.view(-1)
class Model(torch.nn.Module):
def __init__(self, hidden_channels):
super().__init__()
self.encoder = GNNEncoder(hidden_channels, hidden_channels)
self.encoder = to_hetero(self.encoder, data.metadata(), aggr='sum')
self.decoder = EdgeDecoder(hidden_channels)
def forward(self, x_dict, edge_index_dict, edge_label_index):
z_dict = self.encoder(x_dict, edge_index_dict)
return self.decoder(z_dict, edge_label_index)
model = Model(hidden_channels=32).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train():
model.train()
total_loss = total_examples = 0
for batch in train_loader:
batch = batch.to(device)
optimizer.zero_grad()
pred = model(
batch.x_dict,
batch.edge_index_dict,
batch['user', 'movie'].edge_label_index,
)
target = batch['user', 'movie'].edge_label.float()
loss = F.mse_loss(pred, target)
loss.backward()
optimizer.step()
total_loss += float(loss * pred.size(0))
total_examples += pred.size(0)
return total_loss / total_examples
@torch.no_grad()
def test(loader):
model.eval()
preds, targets = [], []
for batch in loader:
batch = batch.to(device)
pred = model(
batch.x_dict,
batch.edge_index_dict,
batch['user', 'movie'].edge_label_index,
).clamp(min=0, max=5)
preds.append(pred)
targets.append(batch['user', 'movie'].edge_label.float())
pred = torch.cat(preds, dim=0)
target = torch.cat(targets, dim=0)
rmse = (pred - target).pow(2).mean().sqrt()
return float(rmse)
for epoch in range(1, 11):
loss = train()
val_rmse = test(val_loader)
test_rmse = test(test_loader)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val RMSE: {val_rmse:.4f}, '
f'Test RMSE: {test_rmse:.4f}')
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