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"""This is an example of using the PGM explainer algorithm on a node
classification task.
"""
import os.path as osp
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.contrib.explain import PGMExplainer
from torch_geometric.datasets import Planetoid
from torch_geometric.explain import Explainer, ModelConfig
from torch_geometric.nn import GCNConv
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Planetoid')
transform = T.Compose([T.GCNNorm(), T.NormalizeFeatures()])
dataset = Planetoid(path, dataset, transform=transform)
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = GCNConv(dataset.num_features, 16, normalize=False)
self.conv2 = GCNConv(16, dataset.num_classes, normalize=False)
def forward(self, x, edge_index, edge_weight):
x = F.relu(self.conv1(x, edge_index, edge_weight))
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index, edge_weight)
return F.log_softmax(x, dim=1)
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
data = data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01,
weight_decay=5e-4)
x, edge_index, edge_weight, target = \
data.x, data.edge_index, data.edge_weight, data.y
model.train()
for epoch in range(1, 500):
optimizer.zero_grad()
log_logits = model(x, edge_index, edge_weight)
loss = F.nll_loss(log_logits[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
model.eval()
log_logits = model(x, edge_index, edge_weight)
predicted_target = log_logits.argmax(dim=1)
explainer = Explainer(
model=model, algorithm=PGMExplainer(), node_mask_type='attributes',
explanation_type='phenomenon',
model_config=ModelConfig(mode='multiclass_classification',
task_level='node', return_type='raw'))
node_idx = 100
explanation = explainer(x=data.x, edge_index=edge_index, index=node_idx,
target=predicted_target, edge_weight=edge_weight)
print(f'Significance of relevant neighbors: {explanation.pgm_stats}')
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