parse.py

from models import LINKX, MLP
from data_utils import normalize

def parse_method(args, dataset, n, c, d):
    if args.method == 'linkx':
        model = LINKX(d, args.hidden_channels, c, args.num_layers, dataset.graph['num_nodes'],
        inner_activation=args.inner_activation, inner_dropout=args.inner_dropout, dropout=args.dropout, init_layers_A=args.link_init_layers_A, init_layers_X=args.link_init_layers_X)
    else:
        raise ValueError('Invalid method')
    return model


def parser_add_main_args(parser):
    parser.add_argument('--dataset', type=str, default='fb100')
    parser.add_argument('--sub_dataset', type=str, default='')
    parser.add_argument('--hidden_channels', type=int, default=32)
    parser.add_argument('--dropout', type=float, default=0.5)
    parser.add_argument('--lr', type=float, default=0.01)
    parser.add_argument('--method', '-m', type=str, default='link')
    parser.add_argument('--epochs', type=int, default=150)
    parser.add_argument('--cpu', action='store_true')
    parser.add_argument('--weight_decay', type=float, default=1e-3)
    parser.add_argument('--display_step', type=int,
                        default=1, help='how often to print')
    parser.add_argument('--hops', type=int, default=1,
                        help='power of adjacency matrix for certain methods')
    parser.add_argument('--num_layers', type=int, default=2,
                        help='number of layers for deep methods')
    parser.add_argument('--runs', type=int, default=1,
                        help='number of distinct runs')
    parser.add_argument('--cached', action='store_true',
                        help='set to use faster sgc')
    parser.add_argument('--gat_heads', type=int, default=8,
                        help='attention heads for gat')
    parser.add_argument('--lp_alpha', type=float, default=.1,
                        help='alpha for label prop')
    parser.add_argument('--gpr_alpha', type=float, default=.1,
                        help='alpha for gprgnn')
    parser.add_argument('--gcn2_alpha', type=float, default=.1,
                        help='alpha for gcn2')
    parser.add_argument('--theta', type=float, default=.5,
                        help='theta for gcn2')
    parser.add_argument('--directed', action='store_true',
                        help='set to not symmetrize adjacency')
    parser.add_argument('--jk_type', type=str, default='max', choices=['max', 'lstm', 'cat'],
                        help='jumping knowledge type')
    parser.add_argument('--rocauc', action='store_true',
                        help='set the eval function to rocauc')
    parser.add_argument('--num_mlp_layers', type=int, default=1,
                        help='number of mlp layers in h2gcn')
    parser.add_argument('--print_prop', action='store_true',
                        help='print proportions of predicted class')
    parser.add_argument('--train_prop', type=float, default=.5,
                        help='training label proportion')
    parser.add_argument('--valid_prop', type=float, default=.25,
                        help='validation label proportion')
    parser.add_argument('--adam', action='store_true', help='use adam instead of adamW')
    parser.add_argument('--rand_split', action='store_true', help='use random splits')
    parser.add_argument('--no_bn', action='store_true', help='do not use batchnorm')
    parser.add_argument('--sampling', action='store_true', help='use neighbor sampling')
    parser.add_argument('--inner_activation', action='store_true', help='Whether linkV3 uses inner activation')
    parser.add_argument('--inner_dropout', action='store_true', help='Whether linkV3 uses inner dropout')
    parser.add_argument("--SGD", action='store_true', help='Use SGD as optimizer')
    parser.add_argument('--link_init_layers_A', type=int, default=1)
    parser.add_argument('--link_init_layers_X', type=int, default=1)