def test_eval_parametric(self):
data = np.array([1, 1, 1, 1, 1, 1, 1], dtype=np.float32).reshape(
(1, 7))
spn = (Gaussian(mean=1.0, stdev=1.0, scope=[0]) *
Exponential(l=1.0, scope=[1]) *
Gamma(alpha=1.0, beta=1.0, scope=[2]) *
LogNormal(mean=1.0, stdev=1.0, scope=[3]) *
Poisson(mean=1.0, scope=[4]) * Bernoulli(p=0.6, scope=[5]) *
Categorical(p=[0.1, 0.2, 0.7], scope=[6]))
ll = log_likelihood(spn, data)
tf_ll = eval_tf(spn, data)
self.assertTrue(np.all(np.isclose(ll, tf_ll)))
spn_copy = Copy(spn)
tf_graph, data_placeholder, variable_dict = spn_to_tf_graph(
spn_copy, data, 1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
tf_graph_to_spn(variable_dict)
str_val = spn_to_str_equation(spn)
str_val2 = spn_to_str_equation(spn_copy)
self.assertEqual(str_val, str_val2)
def test_optimization(self):
np.random.seed(17)
data = np.random.normal(10, 0.01,
size=2000).tolist() + np.random.normal(
30, 10, size=2000).tolist()
data = np.array(data).reshape((-1, 10))
data = data.astype(np.float32)
ds_context = Context(meta_types=[MetaType.REAL] * data.shape[1],
parametric_types=[Gaussian] * data.shape[1])
spn = learn_parametric(data, ds_context)
spn.weights = [0.8, 0.2]
py_ll = log_likelihood(spn, data)
tf_graph, data_placeholder, variable_dict = spn_to_tf_graph(spn, data)
loss = likelihood_loss(tf_graph)
output = tf.train.AdamOptimizer(0.001).minimize(loss)
with tf.Session() as session:
session.run(tf.global_variables_initializer())
for step in range(50):
session.run(output, feed_dict={data_placeholder: data})
# print("loss:", step, session.run(-loss, feed_dict={data_placeholder: data}))
tf_ll_opt = session.run(tf_graph,
feed_dict={
data_placeholder: data
}).reshape(-1, 1)
tf_graph_to_spn(variable_dict)
py_ll_opt = log_likelihood(spn, data)
# print(tf_ll_opt.sum(), py_ll_opt.sum())
self.assertTrue(np.all(np.isclose(tf_ll_opt, py_ll_opt)))
self.assertLess(py_ll.sum(), tf_ll_opt.sum())