SVD_embedding(g, "SVD_OutputFile", size=100)
embedding_look_up = load_embedding("SVD_OutputFile")
result = LinkPrediction(embedding_look_up, G, G_train, testing_pos_edges,0)
auc_roc, auc_pr, accuracy, f1 = result
_results['results'] = dict(
auc_roc=auc_roc,
auc_pr=auc_pr,
accuracy=accuracy,
f1=f1,
)
print(_results)
import time
G, G_train, testing_pos_edges, train_graph_filename = split_train_test_graph(train_graph_filename, None, weighted=None)
g = read_for_OpenNE(train_graph_filename, weighted=None)
model = hope.HOPE(g, 100)
model.save_embeddings("HOPE_OutputFile")
embedding_look_up = load_embedding("HOPE_OutputFile")
result = LinkPrediction(embedding_look_up, G, G_train, testing_pos_edges,0)
auc_roc, auc_pr, accuracy, f1 = result
_results['results'] = dict(
auc_roc=auc_roc,
auc_pr=auc_pr,
accuracy=accuracy,
f1=f1,
)
print(_results)
G, G_train, testing_pos_edges, train_graph_filename = split_train_test_graph(train_graph_filename, None, weighted=None)
g = read_for_OpenNE(train_graph_filename, weighted=None)
model = lap.LaplacianEigenmaps(g, rep_size=100)
def _embedding_training(args, G_=None):
seed=args.seed
if args.method == 'struc2vec':
logging.basicConfig(filename='./src/bionev/struc2vec/struc2vec.log', filemode='w', level=logging.DEBUG,
format='%(asctime)s %(message)s')
if (args.OPT3):
until_layer = args.until_layer
else:
until_layer = None
G = struc2vec.Graph(G_, args.workers, untilLayer=until_layer)
if (args.OPT1):
G.preprocess_neighbors_with_bfs_compact()
else:
G.preprocess_neighbors_with_bfs()
if (args.OPT2):
G.create_vectors()
G.calc_distances(compactDegree=args.OPT1)
else:
G.calc_distances_all_vertices(compactDegree=args.OPT1)
print('create distances network..')
G.create_distances_network()
print('begin random walk...')
G.preprocess_parameters_random_walk()
G.simulate_walks(args.number_walks, args.walk_length)
print('walk finished..\nLearning embeddings...')
walks = LineSentence('random_walks.txt')
model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, hs=1, sg=1,
workers=args.workers, seed=seed)
os.remove("random_walks.txt")
model.wv.save_word2vec_format(args.output)
elif args.method == 'GAE':
model = gae_model(args)
G = G_[0]
node_list = G_[1]
model.train(G)
# save embeddings
model.save_embeddings(args.output, node_list)
elif args.method == 'SVD':
SVD_embedding(G_, args.output, size=args.dimensions)
else:
if args.method == 'Laplacian':
model = lap.LaplacianEigenmaps(G_, rep_size=args.dimensions)
elif args.method == 'GF':
model = gf.GraphFactorization(G_, rep_size=args.dimensions,
epoch=args.epochs, learning_rate=args.lr, weight_decay=args.weight_decay)
elif args.method == 'HOPE':
model = hope.HOPE(graph=G_, d=args.dimensions)
elif args.method == 'GraRep':
model = grarep.GraRep(graph=G_, Kstep=args.kstep, dim=args.dimensions)
elif args.method == 'DeepWalk':
model = node2vec.Node2vec(graph=G_, path_length=args.walk_length,
num_paths=args.number_walks, dim=args.dimensions,
workers=args.workers, window=args.window_size, dw=True)
elif args.method == 'node2vec':
model = node2vec.Node2vec(graph=G_, path_length=args.walk_length,
num_paths=args.number_walks, dim=args.dimensions,
workers=args.workers, p=args.p, q=args.q, window=args.window_size)
elif args.method == 'LINE':
model = line.LINE(G_, epoch=args.epochs,
rep_size=args.dimensions, order=args.order)
elif args.method == 'SDNE':
encoder_layer_list = ast.literal_eval(args.encoder_list)
model = sdne.SDNE(G_, encoder_layer_list=encoder_layer_list,
alpha=args.alpha, beta=args.beta, nu1=args.nu1, nu2=args.nu2,
batch_size=args.bs, epoch=args.epochs, learning_rate=args.lr)
else:
raise ValueError(f'Invalid method: {args.method}')
print("Saving embeddings...")
model.save_embeddings(args.output)
return