dmlc--dgl
Da Zheng
411bcd7e6c
* enable tutorial test in CI. * extand DGLGraph graph_data. * update doc. * Revert "enable tutorial test in CI." This reverts commit cd774067180922bb6ae979bde4aecbffc61c8147. * accept DGLGraph in graph store.
103 行
3.6 KiB
Python
103 行
3.6 KiB
Python
import argparse, time, math
|
|
import numpy as np
|
|
import mxnet as mx
|
|
from mxnet import gluon
|
|
from functools import partial
|
|
import dgl
|
|
import dgl.function as fn
|
|
from dgl import DGLGraph
|
|
from dgl.data import register_data_args, load_data
|
|
from gcn_ns_sc import gcn_ns_train
|
|
from gcn_cv_sc import gcn_cv_train
|
|
from graphsage_cv import graphsage_cv_train
|
|
|
|
|
|
def main(args):
|
|
# load and preprocess dataset
|
|
data = load_data(args)
|
|
|
|
if args.gpu >= 0:
|
|
ctx = mx.gpu(args.gpu)
|
|
else:
|
|
ctx = mx.cpu()
|
|
|
|
if args.self_loop and not args.dataset.startswith('reddit'):
|
|
data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])
|
|
|
|
train_nid = mx.nd.array(np.nonzero(data.train_mask)[0]).astype(np.int64)
|
|
test_nid = mx.nd.array(np.nonzero(data.test_mask)[0]).astype(np.int64)
|
|
|
|
features = mx.nd.array(data.features)
|
|
labels = mx.nd.array(data.labels)
|
|
train_mask = mx.nd.array(data.train_mask)
|
|
val_mask = mx.nd.array(data.val_mask)
|
|
test_mask = mx.nd.array(data.test_mask)
|
|
in_feats = features.shape[1]
|
|
n_classes = data.num_labels
|
|
n_edges = data.graph.number_of_edges()
|
|
|
|
n_train_samples = train_mask.sum().asscalar()
|
|
n_val_samples = val_mask.sum().asscalar()
|
|
n_test_samples = test_mask.sum().asscalar()
|
|
|
|
print("""----Data statistics------'
|
|
#Edges %d
|
|
#Classes %d
|
|
#Train samples %d
|
|
#Val samples %d
|
|
#Test samples %d""" %
|
|
(n_edges, n_classes,
|
|
n_train_samples,
|
|
n_val_samples,
|
|
n_test_samples))
|
|
|
|
# create GCN model
|
|
g = dgl.DGLGraph(data.graph, readonly=True)
|
|
g.ndata['features'] = features
|
|
g.ndata['labels'] = labels
|
|
|
|
if args.model == "gcn_ns":
|
|
gcn_ns_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples)
|
|
elif args.model == "gcn_cv":
|
|
gcn_cv_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples, False)
|
|
elif args.model == "graphsage_cv":
|
|
graphsage_cv_train(g, ctx, args, n_classes, train_nid, test_nid, n_test_samples, False)
|
|
else:
|
|
print("unknown model. Please choose from gcn_ns, gcn_cv, graphsage_cv")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='GCN')
|
|
register_data_args(parser)
|
|
parser.add_argument("--model", type=str,
|
|
help="select a model. Valid models: gcn_ns, gcn_cv, graphsage_cv")
|
|
parser.add_argument("--dropout", type=float, default=0.5,
|
|
help="dropout probability")
|
|
parser.add_argument("--gpu", type=int, default=-1,
|
|
help="gpu")
|
|
parser.add_argument("--lr", type=float, default=3e-2,
|
|
help="learning rate")
|
|
parser.add_argument("--n-epochs", type=int, default=200,
|
|
help="number of training epochs")
|
|
parser.add_argument("--batch-size", type=int, default=1000,
|
|
help="batch size")
|
|
parser.add_argument("--test-batch-size", type=int, default=1000,
|
|
help="test batch size")
|
|
parser.add_argument("--num-neighbors", type=int, default=3,
|
|
help="number of neighbors to be sampled")
|
|
parser.add_argument("--n-hidden", type=int, default=16,
|
|
help="number of hidden gcn units")
|
|
parser.add_argument("--n-layers", type=int, default=1,
|
|
help="number of hidden gcn layers")
|
|
parser.add_argument("--self-loop", action='store_true',
|
|
help="graph self-loop (default=False)")
|
|
parser.add_argument("--weight-decay", type=float, default=5e-4,
|
|
help="Weight for L2 loss")
|
|
parser.add_argument("--nworkers", type=int, default=1,
|
|
help="number of workers")
|
|
args = parser.parse_args()
|
|
|
|
print(args)
|
|
|
|
main(args)
|