def test_tene():
"""
Testing the TENE node embedding.
"""
graph = nx.newman_watts_strogatz_graph(250, 10, 0.2)
features = np.random.uniform(0, 1, (250, 128))
model = TENE()
model.fit(graph, features)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == 2 * model.dimensions
assert type(embedding) == np.ndarray
graph = nx.newman_watts_strogatz_graph(150, 10, 0.2)
features = np.random.uniform(0, 1, (150, 128))
model = TENE(dimensions=8)
model.fit(graph, features)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == 2 * model.dimensions
assert type(embedding) == np.ndarray
model.fit(g) model.get_embedding() #--------------- # TENE example #--------------- g = nx.newman_watts_strogatz_graph(100, 20, 0.05) t = np.random.uniform(0,1,(100,2000)) tp = nx.newman_watts_strogatz_graph(100, 20, 0.05) tp = nx.adjacency_matrix(tp) model = TENE() model.fit(g, t) model.get_embedding() model.fit(g, tp) model.get_embedding() #--------------- # BANE example #--------------- g = nx.newman_watts_strogatz_graph(100, 20, 0.05) x = np.random.uniform(0,1,(100,2000))
import numpy as np
import networkx as nx
from karateclub.node_embedding.attributed import TENE
import dgl
from dgl.data import CoraGraphDataset
data = CoraGraphDataset()
g = data[0]
g = dgl.add_self_loop(
dgl.node_subgraph(g, list(set(np.random.choice(len(g.nodes()), 5)))))
X = np.array(g.ndata['feat'])
g = dgl.to_networkx(g).to_undirected()
# g = nx.newman_watts_strogatz_graph(200, 20, 0.05)
#
# X = np.random.uniform(0, 1, (200, 200))
model = TENE()
model.fit(g, X)
embedding = model.get_embedding()
embedding = np.sum(embedding, axis=0)
# print(np.concatenate((embedding, embedding), axis=0).shape)
print(embedding.shape)
"""TENE example.""" import numpy as np import networkx as nx from karateclub.node_embedding.attributed import TENE g = nx.newman_watts_strogatz_graph(200, 20, 0.05) x = np.random.uniform(0, 1, (200, 200)) model = TENE() model.fit(g, x)
help="the negative slope of leaky relu")
parser.add_argument('--early-stop',
action='store_true',
default=True,
help="indicates whether to use early stop or not")
parser.add_argument('--fastmode',
action="store_true",
default=False,
help="skip re-evaluate the validation set")
args = parser.parse_args()
data = CoraGraphDataset()
dataset = data[0]
dataset = dgl.add_self_loop(dataset)
embedding_model = TENE()
embedding_model.fit(
dgl.to_networkx(dataset).to_undirected(), np.array(dataset.ndata['feat']))
dataset_embedding = embedding_model.get_embedding()
dataset_embedding = np.sum(dataset_embedding, axis=0)
heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
GNN_model = GAT(args.num_layers, dataset.ndata['feat'].shape[1],
args.num_hidden, data.num_labels, heads, F.elu, args.in_drop,
args.attn_drop, args.negative_slope, args.residual)
GNN_model.load_state_dict(torch.load('GAT/es_checkpoint.pt'))
env = myenv(GNN_model, embedding_model, dataset)
env.reset()
# if gpu is to be used