gnn_explainer_link_pred.py

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}')