def main():
"""Main execution steps
Reads arguments, fixes random seeding, executes the HOPE model
and saves resulting embeddings.
"""
args = parse_args()
# Set random seed if specified
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
# numpy seeds need to be in [0, 2^32-1]
args.seed = [
np.random.randint(4294967296 - 1) for i in range(args.num_embeddings)
]
# Parse graph data
graph_name, graph = parse_graph(args.dataset, args.largest_cc)
graph = graph.to_directed()
# Compute embeddings
model = HOPE(d=128, beta=args.beta)
print("Num nodes: %d, num edges: %d" %
(graph.number_of_nodes(), graph.number_of_edges()))
times = []
for i in range(args.num_embeddings):
t1 = time()
# Set the seed before learning
np.random.seed(args.seed[i])
random.seed(args.seed[i])
Y, t = model.learn_embedding(graph=graph,
edge_f=None,
is_weighted=True,
no_python=True)
times.append(time() - t1)
# save embedding
file_path = (f"{args.outputdir}/hope_{graph_name}_" f"{i:03d}.emb")
print(f"Saving embedding to {file_path}")
save_embedding(
Y, file_path, {
"algorithm": "hope",
"dimension": args.dimensions,
"beta": args.beta,
"seed": args.seed[i],
})
print(model._method_name + "\n\tAverage training time: %f" %
(sum(times) / len(times)))
def main(data_set_name):
dimensions = 4
input_file = './graph/' + data_set_name + '.tsv'
output_file = './emb/' + data_set_name + '.emb'
# Instatiate the embedding method with hyperparameters
graph_factorization = HOPE(dimensions, 1)
# Load graph
graph = graph_util.loadGraphFromEdgeListTxt(input_file)
# Learn embedding - accepts a networkx graph or file with edge list
embeddings_array, t = graph_factorization.learn_embedding(graph,
edge_f=None,
is_weighted=True,
no_python=True)
embeddings = pandas.DataFrame(embeddings_array)
embeddings.to_csv(output_file, sep=' ', na_rep=0.1)
def emb(self):
try:
open("emb_" + self.name + ".pkl")
return
except:
res = []
for key in self.keys:
g = self.graph[key]
s = nx.from_dict_of_dicts(g._adj)
if self.type_emb == "hope":
embedding = HOPE(d=self.r_d, beta=0.01)
else:
print("Wrong type!!!")
return
Y, t = embedding.learn_embedding(graph=s,
edge_f=None,
is_weighted=True,
no_python=True)
Y = np.asmatrix(Y)
x = Y.T
f = x * Y
x = f
if self.moment == 3:
x = f * f
x = np.asarray(x)
x = x.reshape(x.size, 1)
res.append(x)
pickle.dump(res, open("emb_" + str(self.name) + ".pkl", "wb"))
# emb=Create_emb("/home/polina/PycharmProjects/Multi_graphs/enron400_graph.pkl","try")
# emb.emb()
def learn_embeddings(self):
hope = HOPE(d=self.dim, beta=0.01)
hope.learn_embedding(self.graph)
self.embeddings = hope.get_embedding()
"""安装GEM的包
https://github.com/palash1992/GEM
"""
#encoding='utf-8'
from gem.utils import graph_util
from gem.evaluation import evaluate_graph_reconstruction as gr
from time import time
from gem.embedding.hope import HOPE
edge_f = './data/load_rename.csv'
isDirected = True
#load graph
G = graph_util.loadGraphFromEdgeListTxt(edge_f, directed=isDirected)
G = G.to_directed()
#模型运行
print('Num nodes: %d, num edges: %d' %
(G.number_of_nodes(), G.number_of_edges()))
t1 = time()
#根据grid_search调参
embedding = HOPE(d=80, beta=0.01)
# Learn embedding - accepts a networkx graph or file with edge list
Y, t = embedding.learn_embedding(graph=G, is_weighted=True)
print(Y)
# print(Y)
# # Evaluate on graph reconstruction
# MAP, prec_curv, err, err_baseline = gr.evaluateStaticGraphReconstruction(G, embedding, Y, None)
is_weighted=True,
no_python=True)
print 'Graph Factorization:\n\tTraining time: %f' % t
# Locally Linear Embedding
embedding = lle(2) # d
Y, t = embedding.learn_embedding(graph=G,
edge_f=None,
is_weighted=True,
no_python=True)
print 'Locally Linear Embedding:\n\tTraining time: %f' % t
# Hope
embedding = HOPE(4, 0.01) # d, beta
Y, t = embedding.learn_embedding(graph=G,
edge_f=None,
is_weighted=True,
no_python=True)
print 'HOPE:\n\tTraining time: %f' % t
# Laplacian Eigen maps
embedding = lap(4) # d
Y, t = embedding.learn_embedding(graph=G,
edge_f=None,
is_weighted=True,
no_python=True)
print 'Laplacian Eigenmaps:\n\tTraining time: %f' % t
# sdne
embedding = SDNE(d=2,
beta=5,
alpha=1e-5,
