def test_log_likelihood_sym(self, output_dim, input_shape):
bmr = BernoulliMLPRegressor(input_shape=(input_shape[1], ),
output_dim=output_dim)
new_xs_var = tf.compat.v1.placeholder(tf.float32, input_shape)
new_ys_var = tf.compat.v1.placeholder(dtype=tf.float32,
name='ys',
shape=(None, output_dim))
data = np.full(input_shape, 0.5)
one_hot_label = np.zeros((input_shape[0], output_dim))
one_hot_label[np.arange(input_shape[0]), 0] = 1
p = bmr._f_prob(np.asarray(data))
ll = bmr._dist.log_likelihood(np.asarray(one_hot_label), dict(p=p))
outputs = bmr.log_likelihood_sym(new_xs_var, new_ys_var, name='ll_sym')
ll_from_sym = self.sess.run(outputs,
feed_dict={
new_xs_var: data,
new_ys_var: one_hot_label
})
assert np.allclose(ll, ll_from_sym, rtol=0, atol=1e-5)
def test_sample_predict(self):
n_sample = 100
input_dim = 50
output_dim = 1
bmr = BernoulliMLPRegressor(input_shape=(input_dim, ),
output_dim=output_dim)
xs = np.random.random((input_dim, ))
p = bmr._f_prob([xs])
ys = bmr.sample_predict([xs] * n_sample)
p_predict = np.count_nonzero(ys == 1) / n_sample
assert np.real_if_close(p, p_predict)
def test_predict_log_likelihood(self):
n_sample = 50
input_dim = 50
output_dim = 1
bmr = BernoulliMLPRegressor(input_shape=(input_dim, ),
output_dim=output_dim)
xs = np.random.random((n_sample, input_dim))
ys = np.random.randint(2, size=(n_sample, output_dim))
p = bmr._f_prob(xs)
ll = bmr.predict_log_likelihood(xs, ys)
ll_true = np.sum(np.log(p * ys + (1 - p) * (1 - ys)), axis=-1)
assert np.allclose(ll, ll_true)