def test_output_sets_alpha(labels, hybridize):
binned_output = BinnedOutput(bin_centers=COMMON_KWARGS["bin_centers"][0],
label_smoothing=0.35)
arg_proj = binned_output.get_args_proj()
if hybridize:
arg_proj.hybridize()
arg_proj.initialize()
assert (binned_output.distribution(arg_proj(mx.nd.random.uniform(
2, 10))).label_smoothing == 0.35)
def test_binned_likelihood(num_bins: float, bin_probabilites: np.ndarray,
hybridize: bool):
'''
Test to check that maximizing the likelihood recovers the parameters
'''
bin_prob = mx.nd.array(bin_probabilites)
bin_center = mx.nd.array(np.logspace(-1, 1, num_bins))
# generate samples
bin_probs = mx.nd.zeros((NUM_SAMPLES, num_bins)) + bin_prob
bin_centers = mx.nd.zeros((NUM_SAMPLES, num_bins)) + bin_center
distr = Binned(bin_probs, bin_centers)
samples = distr.sample()
# add some jitter to the uniform initialization and normalize
bin_prob_init = mx.nd.random_uniform(1 - TOL, 1 + TOL, num_bins) * bin_prob
bin_prob_init = bin_prob_init / bin_prob_init.sum()
init_biases = [bin_prob_init]
bin_prob_hat, = maximum_likelihood_estimate_sgd(
BinnedOutput(list(bin_center.asnumpy())),
samples,
init_biases=init_biases,
hybridize=hybridize,
learning_rate=PositiveFloat(0.05),
num_epochs=PositiveInt(25),
)
assert all(
mx.nd.abs(mx.nd.array(bin_prob_hat) - bin_prob) < TOL * bin_prob
), f"bin_prob did not match: bin_prob = {bin_prob}, bin_prob_hat = {bin_prob_hat}"