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import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.explain import Explainer, GraphMaskExplainer
from torch_geometric.nn import GATConv, GCNConv
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
path = osp.join(osp.dirname(osp.realpath(__file__)), 'data', 'Planetoid')
dataset = Planetoid(path, name='Cora')
data = dataset[0].to(device)
# GCN Node Classification =====================================================
class GCN(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(dataset.num_features, 16)
self.conv2 = GCNConv(16, dataset.num_classes)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
model = GCN().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
for epoch in range(1, 201):
model.train()
optimizer.zero_grad()
out = model(data.x, data.edge_index)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
explainer = Explainer(
model=model,
algorithm=GraphMaskExplainer(2, epochs=5),
explanation_type='model',
node_mask_type='attributes',
edge_mask_type='object',
model_config=dict(
mode='multiclass_classification',
task_level='node',
return_type='log_probs',
),
)
node_index = 10
explanation = explainer(data.x, data.edge_index, index=node_index)
print(f'Generated explanations in {explanation.available_explanations}')
# GAT Node Classification =====================================================
class GAT(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GATConv(dataset.num_features, 8, heads=8)
self.conv2 = GATConv(64, dataset.num_classes, heads=1, concat=False)
def forward(self, x, edge_index):
x = self.conv1(x, edge_index).relu()
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
model = GAT().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)
for epoch in range(1, 201):
model.train()
optimizer.zero_grad()
out = model(data.x, data.edge_index)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
explainer = Explainer(
model=model,
algorithm=GraphMaskExplainer(2, epochs=5),
explanation_type='model',
node_mask_type='attributes',
edge_mask_type='object',
model_config=dict(
mode='multiclass_classification',
task_level='node',
return_type='log_probs',
),
)
node_index = torch.tensor([10, 20])
explanation = explainer(data.x, data.edge_index, index=node_index)
print(f'Generated explanations in {explanation.available_explanations}')
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