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"""This is an example of using the PGM explainer algorithm on a graph
classification task.
"""
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import Linear, ReLU, Sequential
import torch_geometric.transforms as T
from torch_geometric.contrib.explain import PGMExplainer
from torch_geometric.datasets import MNISTSuperpixels
from torch_geometric.explain import Explainer
from torch_geometric.loader import DataLoader
from torch_geometric.nn import (
NNConv,
global_mean_pool,
graclus,
max_pool,
max_pool_x,
)
from torch_geometric.utils import normalized_cut
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'MNIST')
transform = T.Cartesian(cat=False)
train_dataset = MNISTSuperpixels(path, True, transform=transform)
test_dataset = MNISTSuperpixels(path, False, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=512, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False)
d = train_dataset
def normalized_cut_2d(edge_index, pos):
row, col = edge_index
edge_attr = torch.norm(pos[row] - pos[col], p=2, dim=1)
return normalized_cut(edge_index, edge_attr, num_nodes=pos.size(0))
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
nn1 = Sequential(
Linear(2, 25),
ReLU(),
Linear(25, d.num_features * 32),
)
self.conv1 = NNConv(d.num_features, 32, nn1, aggr='mean')
nn2 = Sequential(
Linear(2, 25),
ReLU(),
Linear(25, 32 * 64),
)
self.conv2 = NNConv(32, 64, nn2, aggr='mean')
self.fc1 = torch.nn.Linear(64, 128)
self.fc2 = torch.nn.Linear(128, d.num_classes)
def forward(self, x, edge_index, **kwargs):
data = kwargs.get('data')
data = data.detach().clone()
x = F.elu(self.conv1(x, edge_index, data.edge_attr))
weight = normalized_cut_2d(edge_index, data.pos)
cluster = graclus(edge_index, weight, x.size(0))
data.edge_attr = None
data.x = x
data.edge_index = edge_index
data = max_pool(cluster, data, transform=transform)
data.x = F.elu(self.conv2(data.x, data.edge_index, data.edge_attr))
weight = normalized_cut_2d(data.edge_index, data.pos)
cluster = graclus(data.edge_index, weight, data.x.size(0))
x, batch = max_pool_x(cluster, data.x, data.batch)
x = global_mean_pool(x, batch)
x = F.elu(self.fc1(x))
x = F.dropout(x, training=self.training)
return F.log_softmax(self.fc2(x), dim=1)
def train(model, dataloader):
model.train()
for data in dataloader:
data = data.to(device)
optimizer.zero_grad()
F.nll_loss(model(data.x, data), data.y).backward()
optimizer.step()
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'current device: {device}')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(2):
train(model, train_loader)
explainer = Explainer(
model=model, algorithm=PGMExplainer(perturb_feature_list=[0],
perturbation_mode="mean"),
explanation_type='phenomenon', node_mask_type="object",
model_config=dict(mode="multiclass_classification", task_level="graph",
return_type="raw"))
i = 0
for explain_dataset in test_loader:
explain_dataset.to(device)
explanation = explainer(x=explain_dataset.x,
edge_index=explain_dataset.edge_index,
target=explain_dataset.y,
edge_attr=explain_dataset.edge_attr,
data=explain_dataset)
for k in explanation.available_explanations:
print(explanation[k])
i += 1
if i > 2:
break
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