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
sdne = SDNE(dimensions)
# Load graph
graph = graph_util.loadGraphFromEdgeListTxt(input_file)
# Learn embedding - accepts a networkx graph or file with edge list
embeddings_array, t = sdne.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)
nu2=1e-6,
K=3,
n_units=[
50,
15,
],
rho=0.3,
n_iter=50,
xeta=0.01,
n_batch=500,
modelfile=[
'./outdata/intermediate/enc_model.json',
'./outdata/intermediate/dec_model.json'
],
weightfile=[
'./outdata/intermediate/enc_weights.hdf5',
'./outdata/intermediate/dec_weights.hdf5'
])
# Learn embedding - accepts a networkx graph or file with edge list
Y, t = embedding.learn_embedding(graph=G, is_weighted=True)
# ev.evaluateNodeClassification()
# print(Y)
viz.plot_embedding2D(embedding.get_embedding(), di_graph=G, node_colors=None)
plt.show()
# print(Y)
# # Evaluate on graph reconstruction
# MAP, prec_curv, err, err_baseline = gr.evaluateStaticGraphReconstruction(G, embedding, Y, None)
