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import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Embedding, Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import MovieLens
from torch_geometric.nn import SAGEConv
from torch_geometric.nn.conv.gcn_conv import gcn_norm
path = osp.join(osp.dirname(osp.realpath(__file__)), '../../data/MovieLens')
dataset = MovieLens(path, model_name='all-MiniLM-L6-v2')
data = dataset[0]
data['user'].x = torch.arange(data['user'].num_nodes)
data['user', 'movie'].edge_label = data['user', 'movie'].edge_label.float()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = data.to(device)
# 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 link-level split into training, validation, and test edges:
train_data, val_data, test_data = T.RandomLinkSplit(
num_val=0.1,
num_test=0.1,
neg_sampling_ratio=0.0,
edge_types=[('user', 'rates', 'movie')],
rev_edge_types=[('movie', 'rev_rates', 'user')],
)(data)
# Generate the co-occurence matrix of movies<>movies:
metapath = [('movie', 'rev_rates', 'user'), ('user', 'rates', 'movie')]
train_data = T.AddMetaPaths(metapaths=[metapath])(train_data)
# Apply normalization to filter the metapath:
_, edge_weight = gcn_norm(
train_data['movie', 'movie'].edge_index,
num_nodes=train_data['movie'].num_nodes,
add_self_loops=False,
)
edge_index = train_data['movie', 'movie'].edge_index[:, edge_weight > 0.002]
train_data['movie', 'metapath_0', 'movie'].edge_index = edge_index
val_data['movie', 'metapath_0', 'movie'].edge_index = edge_index
test_data['movie', 'metapath_0', 'movie'].edge_index = edge_index
class MovieGNNEncoder(torch.nn.Module):
def __init__(self, hidden_channels, out_channels):
super().__init__()
self.conv1 = SAGEConv(-1, hidden_channels)
self.conv2 = SAGEConv(hidden_channels, hidden_channels)
self.lin = Linear(hidden_channels, out_channels)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index).relu()
return self.lin(x)
class UserGNNEncoder(torch.nn.Module):
def __init__(self, hidden_channels, out_channels):
super().__init__()
self.conv1 = SAGEConv((-1, -1), hidden_channels)
self.conv2 = SAGEConv((-1, -1), hidden_channels)
self.conv3 = SAGEConv((-1, -1), hidden_channels)
self.lin = Linear(hidden_channels, out_channels)
def forward(self, x_dict, edge_index_dict):
movie_x = self.conv1(
x_dict['movie'],
edge_index_dict[('movie', 'metapath_0', 'movie')],
).relu()
user_x = self.conv2(
(x_dict['movie'], x_dict['user']),
edge_index_dict[('movie', 'rev_rates', 'user')],
).relu()
user_x = self.conv3(
(movie_x, user_x),
edge_index_dict[('movie', 'rev_rates', 'user')],
).relu()
return self.lin(user_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_src, z_dst, edge_label_index):
row, col = edge_label_index
z = torch.cat([z_src[row], z_dst[col]], dim=-1)
z = self.lin1(z).relu()
z = self.lin2(z)
return z.view(-1)
class Model(torch.nn.Module):
def __init__(self, num_users, hidden_channels, out_channels):
super().__init__()
self.user_emb = Embedding(num_users, hidden_channels)
self.user_encoder = UserGNNEncoder(hidden_channels, out_channels)
self.movie_encoder = MovieGNNEncoder(hidden_channels, out_channels)
self.decoder = EdgeDecoder(out_channels)
def forward(self, x_dict, edge_index_dict, edge_label_index):
z_dict = {}
x_dict['user'] = self.user_emb(x_dict['user'])
z_dict['user'] = self.user_encoder(x_dict, edge_index_dict)
z_dict['movie'] = self.movie_encoder(
x_dict['movie'],
edge_index_dict[('movie', 'metapath_0', 'movie')],
)
return self.decoder(z_dict['user'], z_dict['movie'], edge_label_index)
model = Model(data['user'].num_nodes, hidden_channels=64, out_channels=64)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0003)
def train():
model.train()
optimizer.zero_grad()
out = model(
train_data.x_dict,
train_data.edge_index_dict,
train_data['user', 'movie'].edge_label_index,
)
loss = F.mse_loss(out, train_data['user', 'movie'].edge_label)
loss.backward()
optimizer.step()
return float(loss)
@torch.no_grad()
def test(data):
model.eval()
out = model(
data.x_dict,
data.edge_index_dict,
data['user', 'movie'].edge_label_index,
).clamp(min=0, max=5)
rmse = F.mse_loss(out, data['user', 'movie'].edge_label).sqrt()
return float(rmse)
for epoch in range(1, 701):
loss = train()
train_rmse = test(train_data)
val_rmse = test(val_data)
test_rmse = test(test_data)
print(f'Epoch: {epoch:04d}, Loss: {loss:.4f}, Train: {train_rmse:.4f}, '
f'Val: {val_rmse:.4f}, Test: {test_rmse:.4f}')
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