def simulate_walks(self,
num_walks,
walk_length,
stay_prob=0.3,
workers=1,
verbose=0):
layers_adj = pd.read_pickle(self.temp_path + 'layers_adj.pkl')
layers_alias = pd.read_pickle(self.temp_path + 'layers_alias.pkl')
layers_accept = pd.read_pickle(self.temp_path + 'layers_accept.pkl')
gamma = pd.read_pickle(self.temp_path + 'gamma.pkl')
walks = []
initialLayer = 0
nodes = self.idx # list(self.g.nodes())
results = Parallel(
n_jobs=workers,
verbose=verbose,
)(delayed(self._simulate_walks)
(nodes, num, walk_length, stay_prob, layers_adj, layers_accept,
layers_alias, gamma) for num in partition_num(num_walks, workers))
walks = list(itertools.chain(*results))
return walks
def parallel_walks(self, num_walks, walk_length, workers=1, verbose=0):
G = self.G
nodes = list(G.nodes())
results = Parallel(n_jobs=workers, verbose=verbose, )(
delayed(self._parallel_walk)(nodes,num, walk_length) for num in
partition_num(num_walks, workers))
walks = list(itertools.chain(*results))
return walks