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def visualize(dataset, save_path, name='vis', number=20, loss=None, sort=True):
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if loss is not None:
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assert(len(loss) == len(dataset))
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if sort:
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order = np.argsort(loss.flatten()).tolist()
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else:
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order = list(range(len(loss.flatten())))
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loader = [dataset.get(i) for i in order[-number:][::-1]]
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#loss = [loss[i] for i in order[::-1]]
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loss = [loss[i] for i in order]
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else:
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loader = DataLoader(dataset, batch_size=1, shuffle=False)
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for idx, data in enumerate(loader):
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f = plt.figure(figsize=(20, 20))
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limits = plt.axis('off')
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if 'name' in data.keys:
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del data.name
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if args.h is not None:
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node_size = 150
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with_labels = True
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G = to_networkx(data, node_attrs=['z'])
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labels = {i: G.nodes[i]['z'] for i in range(len(G))}
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else:
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node_size = 300
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with_labels = True
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data.x = data.x[:, 0]
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G = to_networkx(data, node_attrs=['x'])
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labels = {i: G.nodes[i]['x'] for i in range(len(G))}
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if loss is not None:
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label = 'Loss = ' + str(loss[idx])
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print(label)
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else:
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label = ''
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nx.draw_networkx(G, node_size=node_size, arrows=True, with_labels=with_labels,
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labels=labels)
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plt.title(label)
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f.savefig(os.path.join(save_path, f'{name}_{idx}.png'))
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if (idx+1) % 5 == 0:
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pdb.set_trace()
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# General settings.
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parser = argparse.ArgumentParser(description='Nested GNN for OGB molecular graphs')
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parser.add_argument('--dataset', type=str, default="ogbg-molhiv",
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help='dataset name (ogbg-molhiv, ogbg-molpcba, etc.)')
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parser.add_argument('--runs', type=int, default=1, help='how many repeated runs')
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# Base GNN settings.
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parser.add_argument('--gnn', type=str, default='gin',
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help='gin, gcn, ppgn, gine+')
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parser.add_argument('--virtual_node', type=bool, default=True,
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help='enable using virtual node, default true')
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parser.add_argument('--residual', action='store_true', default=False,
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help='enable residual connections between layers')
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parser.add_argument('--RNI', action='store_true', default=False,
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help='use randomly initialized node features in [-1, 1]')
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parser.add_argument('--adj_dropout', type=float, default=0,
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help='adjacency matrix dropout ratio (default: 0)')
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parser.add_argument('--drop_ratio', type=float, default=0.5,
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help='dropout ratio (default: 0.5)')
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parser.add_argument('--num_layer', type=int, default=5,
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help='number of GNN message passing layers (default: 5)')
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parser.add_argument('--emb_dim', type=int, default=300,
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help='dimensionality of hidden units in GNNs (default: 300)')
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# Nested GNN settings.
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parser.add_argument('--h', type=int, default=None, help='height of rooted subgraph;\
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if not None, will extract h-hop rooted subgraphs and use Nested GNN')
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parser.add_argument('--subgraph_pooling', type=str, default="mean",
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help='mean, sum, center, max, attention')
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parser.add_argument('--graph_pooling', type=str, default="mean",
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help='mean, sum, set2set, max, attention')
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parser.add_argument('--node_label', type=str, default='spd',
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help='apply distance encoding to nodes within each subgraph, use node\
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labels as additional node features; support "hop", "drnl", "spd", \
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for "spd", you can specify number of spd to keep by "spd3", "spd4", \
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"spd5", etc. Default "spd"=="spd2".')
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parser.add_argument('--use_rd', action='store_true', default=False,
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help='use resistance distance as additional continuous node labels')
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parser.add_argument('--use_rp', type=int, default=None,
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help='use RW return probability as additional node features,\
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specify num of RW steps here')
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# Training settings.
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parser.add_argument('--batch_size', type=int, default=32,
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help='input batch size for training (default: 32)')
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parser.add_argument('--epochs', type=int, default=100,
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help='number of epochs to train (default: 100)')
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parser.add_argument('--lr', type=float, default=1E-3)
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parser.add_argument('--lr_decay_factor', type=float, default=0.5)
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parser.add_argument('--num_workers', type=int, default=2,
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help='number of workers (default: 2)')
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parser.add_argument('--ensemble', action='store_true', default=False,
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help='load a series of model checkpoints and ensemble the results')
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parser.add_argument('--ensemble_lookback', type=int, default=90,
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help='how many epochs to look back in ensemble')
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parser.add_argument('--ensemble_interval', type=int, default=10,
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help='ensemble every x epochs')
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