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import os.path as osp
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
from torch_geometric.explain import Explainer, GNNExplainer, ModelConfig
from torch_geometric.nn import 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')
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
transform = T.Compose([
T.NormalizeFeatures(),
T.ToDevice(device),
T.RandomLinkSplit(num_val=0.05, num_test=0.1, is_undirected=True),
])
dataset = Planetoid(path, dataset, transform=transform)
train_data, val_data, test_data = dataset[0]
class GCN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
self.conv2 = GCNConv(hidden_channels, out_channels)
def encode(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = self.conv2(x, edge_index)
return x
def decode(self, z, edge_label_index):
src, dst = edge_label_index
return (z[src] * z[dst]).sum(dim=-1)
def forward(self, x, edge_index, edge_label_index):
z = model.encode(x, edge_index)
return model.decode(z, edge_label_index).view(-1)
model = GCN(dataset.num_features, 128, 64).to(device)
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01)
def train():
model.train()
optimizer.zero_grad()
out = model(train_data.x, train_data.edge_index,
train_data.edge_label_index)
loss = F.binary_cross_entropy_with_logits(out, train_data.edge_label)
loss.backward()
optimizer.step()
return float(loss)
@torch.no_grad()
def test(data):
model.eval()
out = model(data.x, data.edge_index, data.edge_label_index).sigmoid()
return roc_auc_score(data.edge_label.cpu().numpy(), out.cpu().numpy())
for epoch in range(1, 201):
loss = train()
if epoch % 20 == 0:
val_auc = test(val_data)
test_auc = test(test_data)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_auc:.4f}, '
f'Test: {test_auc:.4f}')
model_config = ModelConfig(
mode='binary_classification',
task_level='edge',
return_type='raw',
)
# Explain model output for a single edge:
edge_label_index = val_data.edge_label_index[:, 0]
explainer = Explainer(
model=model,
explanation_type='model',
algorithm=GNNExplainer(epochs=200),
node_mask_type='attributes',
edge_mask_type='object',
model_config=model_config,
)
explanation = explainer(
x=train_data.x,
edge_index=train_data.edge_index,
edge_label_index=edge_label_index,
)
print(f'Generated model explanations in {explanation.available_explanations}')
# Explain a selected target (phenomenon) for a single edge:
edge_label_index = val_data.edge_label_index[:, 0]
target = val_data.edge_label[0].unsqueeze(dim=0).long()
explainer = Explainer(
model=model,
explanation_type='phenomenon',
algorithm=GNNExplainer(epochs=200),
node_mask_type='attributes',
edge_mask_type='object',
model_config=model_config,
)
explanation = explainer(
x=train_data.x,
edge_index=train_data.edge_index,
target=target,
edge_label_index=edge_label_index,
)
available_explanations = explanation.available_explanations
print(f'Generated phenomenon explanations in {available_explanations}')
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