def test_all_distributions_either_work_or_raise_error(self, dist_name, data):
if not tf.executing_eagerly():
self.skipTest('No need to test every distribution in graph mode.')
if dist_name in PRECONDITIONING_FAILS_DISTS:
self.skipTest('Known failure.')
dist = data.draw(dhps.base_distributions(
dist_name=dist_name,
enable_vars=False,
param_strategy_fn=_constrained_zeros_fn))
try:
b = tfp.experimental.bijectors.make_distribution_bijector(dist)
except NotImplementedError:
# Okay to fail as long as explicit error is raised.
self.skipTest('Bijector not implemented.')
self.assertDistributionIsApproximatelyStandardNormal(tfb.Invert(b)(dist))
def testDistribution(self, dist_name, data):
dist = data.draw(
dhps.base_distributions(
dist_name=dist_name,
enable_vars=False,
# Unregularized MLEs can be numerically problematic, e.g., empirical
# (co)variances can be singular. To avoid such numerical issues, we
# sanity-check the MLE only for a fixed sample with assumed-sane
# parameter values (zeros constrained to the parameter support).
param_strategy_fn=_constrained_zeros_fn,
batch_shape=data.draw(
tfp_hps.shapes(min_ndims=0, max_ndims=2, max_side=5))))
x, lp = self.evaluate(
dist.experimental_sample_and_log_prob(
10, seed=test_util.test_seed(sampler_type='stateless')))
try:
parameters = self.evaluate(
type(dist)._maximum_likelihood_parameters(x))
except NotImplementedError:
self.skipTest('Fitting not implemented.')
flat_params = tf.nest.flatten(parameters)
lp_fn = lambda *flat_params: type(dist)( # pylint: disable=g-long-lambda
validate_args=True,
**tf.nest.pack_sequence_as(parameters, flat_params)).log_prob(x)
lp_mle, grads = self.evaluate(
tfp_math.value_and_gradient(lp_fn, flat_params))
# Likelihood of MLE params should be higher than of the original params.
self.assertAllGreaterEqual(
tf.reduce_sum(lp_mle, axis=0) - tf.reduce_sum(lp, axis=0), -1e-4)
if dist_name not in MLE_AT_CONSTRAINT_BOUNDARY:
# MLE parameters should be a critical point of the log prob.
for g in grads:
if np.any(np.isnan(g)):
# Skip parameters with undefined or unstable gradients (e.g.,
# Categorical `num_classes`).
continue
self.assertAllClose(tf.zeros_like(g), g, atol=1e-2)
def testDistributionWithVars(self, dist_name, data):
dist = data.draw(
dhps.base_distributions(dist_name=dist_name, enable_vars=True))
self.evaluate([var.initializer for var in dist.variables])
self.check_bad_loc_scale(dist)
self.check_event_space_bijector_constrains(dist, data)
