explain_main.py

import argparse
import os
import dgl
from gnnlens import Writer
import torch as th

from dgl import load_graphs
from dgl.nn import GNNExplainer
from models import Model

from dgl.data import BAShapeDataset, BACommunityDataset, TreeCycleDataset, TreeGridDataset

def main(args):
    if args.dataset == 'BAShape':
        dataset = BAShapeDataset(seed=0)
    elif args.dataset == 'BACommunity':
        dataset = BACommunityDataset(seed=0)
    elif args.dataset == 'TreeCycle':
        dataset = TreeCycleDataset(seed=0)
    elif args.dataset == 'TreeGrid':
        dataset = TreeGridDataset(seed=0)

    graph = dataset[0]
    labels = graph.ndata['label']
    feats = graph.ndata['feat']
    num_classes = dataset.num_classes

    # load an existing model
    model_path = os.path.join('./', f'model_{args.dataset}.pth')
    model_stat_dict = th.load(model_path)
    model = Model(feats.shape[-1], num_classes)
    model.load_state_dict(model_stat_dict)

    # Choose the first node of the class 1 for explaining prediction
    target_class = 1
    for n_idx, n_label in enumerate(labels):
        if n_label == target_class:
            break

    explainer = GNNExplainer(model, num_hops=3)
    new_center, sub_graph, feat_mask, edge_mask = explainer.explain_node(n_idx, graph, feats)

    # gnnlens2
    # Specify the path to create a new directory for dumping data files.
    writer = Writer('gnn_subgraph')
    writer.add_graph(name=args.dataset, graph=graph,
                     nlabels=labels, num_nlabel_types=num_classes)
    writer.add_subgraph(graph_name=args.dataset,
                        subgraph_name='GNNExplainer',
                        node_id=n_idx,
                        subgraph_nids=sub_graph.ndata[dgl.NID],
                        subgraph_eids=sub_graph.edata[dgl.EID],
                        subgraph_eweights=edge_mask)

    # Finish dumping
    writer.close()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Demo of GNN explainer in DGL')
    parser.add_argument('--dataset', type=str, default='BAShape',
                        choices=['BAShape', 'BACommunity', 'TreeCycle', 'TreeGrid'])
    args = parser.parse_args()
    print(args)

    main(args)