def test_save_load(self):
"""
Test if saving and loading the model in a new object gives the same results
"""
filename = os.getcwd() + "/data/test_save_load"
graph = gb.create_directed_barbell(4, 4)
gae = GraphAutoEncoder(graph,
learning_rate=0.01,
support_size=[5, 5],
dims=[3, 5, 7, 6, 2],
batch_size=12,
max_total_steps=50,
verbose=True)
gae.fit(graph)
embed = gae.calculate_embeddings()
gae.save_model(filename)
gae2 = GraphAutoEncoder(graph,
learning_rate=0.01,
support_size=[5, 5],
dims=[3, 5, 7, 6, 2],
batch_size=12,
max_total_steps=50,
verbose=True)
gae2.load_model(filename, graph)
embed2 = gae2.calculate_embeddings()
embed3 = np.subtract(embed, embed2)
self.assertAlmostEqual(
np.sum(embed3), 0, 4,
"loaded model gives different result then original")
d['weight'] = ndic[u] * ndic[v]
#%% create and train model
gae = GraphAutoEncoder(graph, learning_rate=0.01, support_size=[5, 5], dims=[3, 5, 7, 6, 2],
batch_size=30, max_total_steps=1000, verbose=True, act=tf.nn.tanh)
if TRAIN:
train_res = {}
for i in range(len(gae.dims)):
if i in [1, 2]:
train_res["l"+str(i+1)] = gae.train_layer(i+1, dropout=0.1)
else:
train_res["l"+str(i+1)] = gae.train_layer(i+1)
train_res['all'] = gae.train_layer(len(gae.dims), all_layers=True)
pickle.dump(train_res, open(RESULTS_FILE, "wb"))
gae.save_model(MODEL_FILENAME)
else:
gae.load_model(MODEL_FILENAME, graph)
embed = gae.calculate_embeddings()
# %% get tabel with node details
indeg = graph.in_degree()
outdeg = graph.out_degree()
tbl = np.array([[y, x['label1'], x['label2'], indeg[y], outdeg[y], embed[y, 1], embed[y, 2]]
for y, x in graph.nodes(data=True)])
pd_tbl = pd.DataFrame(tbl[:, 1:], tbl[:, 0],
['label1', 'label2', 'in_degree', 'out_degree', 'embed1', 'embed2'])
print(pd_tbl)