import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn.linear_model import LogisticRegression
from torch_geometric.nn import GCNConv, DeepGraphInfomax
from .. import BaseModel, register_model
[docs]class Encoder(nn.Module):
def __init__(self, in_channels, hidden_channels):
super(Encoder, self).__init__()
self.conv = GCNConv(in_channels, hidden_channels, cached=True)
self.prelu = nn.PReLU(hidden_channels)
[docs] def forward(self, x, edge_index):
x = self.conv(x, edge_index)
x = self.prelu(x)
return x
[docs]def corruption(x, edge_index):
return x[torch.randperm(x.size(0))], edge_index
[docs]@register_model("infomax")
class Infomax(BaseModel):
@staticmethod
[docs] def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument("--num-features", type=int)
parser.add_argument("--num-classes", type=int)
parser.add_argument("--hidden-size", type=int, default=512)
# fmt: on
@classmethod
[docs] def build_model_from_args(cls, args):
return cls(
args.num_features,
args.num_classes,
args.hidden_size,
)
def __init__(self, num_features, num_classes, hidden_size):
super(Infomax, self).__init__()
self.num_features = num_features
self.num_classes = num_classes
self.hidden_size = hidden_size
self.model = DeepGraphInfomax(
hidden_channels=hidden_size, encoder=Encoder(num_features, hidden_size),
summary=lambda z, *args, **kwargs: torch.sigmoid(z.mean(dim=0)),
corruption=corruption)
[docs] def forward(self, x, edge_index):
return self.model(x, edge_index)
[docs] def loss(self, data):
pos_z, neg_z, summary = self.forward(data.x, data.edge_index)
loss = self.model.loss(pos_z, neg_z, summary)
return loss
[docs] def predict(self, data):
z, _, _ = self.forward(data.x, data.edge_index)
clf = LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=150)
clf.fit(z[data.train_mask].detach().cpu().numpy(), data.y[data.train_mask].detach().cpu().numpy())
logits = torch.Tensor(clf.predict_proba(z.detach().cpu().numpy()))
if z.is_cuda:
logits = logits.cuda()
return logits