def test_against_numpy(self, data):
dtype = data.draw(hps.sampled_from([np.float32, np.float64]))
shp = (data.draw(hps.integers(5, 10)), data.draw(hps.integers(5, 10)))
axis = data.draw(hps.integers(0, len(shp) - 1))
y = data.draw(
tfp_hps.constrained_tensors(tfp_hps.identity_fn, shp, dtype))
x_dx = data.draw(hps.sampled_from(['x', 'dx', None]))
if x_dx is None:
x = None
dx = None
elif x_dx == 'dx':
x = None
dx = data.draw(hps.floats(0.1, 10))
else:
x = data.draw(
tfp_hps.constrained_tensors(tfp_hps.identity_fn, shp, dtype))
dx = None
np_soln = np.trapz(
self.evaluate(y),
x=self.evaluate(x)
if x is not None else None, # cannot evaluate(None)
dx=dx or 1.0, # numpy default is 1.0
axis=axis)
tf_soln = tfp_math.trapz(y, x, dx, axis)
self.assertAllClose(np_soln, tf_soln)
def check_event_space_bijector_constrains(self, dist, data):
event_space_bijector = dist.experimental_default_event_space_bijector()
if event_space_bijector is None:
return
total_sample_shape = tensorshape_util.concatenate(
# Draw a sample shape
data.draw(tfp_hps.shapes()),
# Draw a shape that broadcasts with `[batch_shape, inverse_event_shape]`
# where `inverse_event_shape` is the event shape in the bijector's
# domain. This is the shape of `y` in R**n, such that
# x = event_space_bijector(y) has the event shape of the distribution.
data.draw(
tfp_hps.broadcasting_shapes(tensorshape_util.concatenate(
dist.batch_shape,
event_space_bijector.inverse_event_shape(
dist.event_shape)),
n=1))[0])
y = data.draw(
tfp_hps.constrained_tensors(tfp_hps.identity_fn,
total_sample_shape.as_list()))
with tfp_hps.no_tf_rank_errors():
x = event_space_bijector(y)
with tf.control_dependencies(dist._sample_control_dependencies(x)):
self.evaluate(tf.identity(x))
def codomain_tensors(draw, bijector, shape=None):
"""Strategy for drawing Tensors in the codomain of a bijector.
If the bijector's codomain is constrained, this proceeds by drawing an
unconstrained Tensor and then transforming it to fit. The constraints are
declared in `bijectors.hypothesis_testlib.bijector_supports`. The
transformations are defined by `tfp_hps.constrainer`.
Args:
draw: Hypothesis strategy sampler supplied by `@hps.composite`.
bijector: A `Bijector` in whose codomain the Tensors will be.
shape: An optional `TensorShape`. The shape of the resulting
Tensors. Hypothesis will pick one if omitted.
Returns:
tensors: A strategy for drawing codomain Tensors for the desired bijector.
"""
if is_invert(bijector):
return draw(domain_tensors(bijector.bijector, shape))
elif is_transform_diagonal(bijector):
return draw(codomain_tensors(bijector.diag_bijector, shape))
if shape is None:
shape = draw(tfp_hps.shapes())
bijector_name = type(bijector).__name__
support = bhps.bijector_supports()[bijector_name].inverse
if is_generalized_pareto(bijector):
constraint_fn = bhps.generalized_pareto_constraint(
bijector.loc, bijector.scale, bijector.concentration)
elif isinstance(bijector, tfb.SoftClip):
constraint_fn = bhps.softclip_constraint(bijector.low, bijector.high)
else:
constraint_fn = tfp_hps.constrainer(support)
return draw(tfp_hps.constrained_tensors(constraint_fn, shape))
def domain_tensors(draw, bijector, shape=None):
"""Strategy for drawing Tensors in the domain of a bijector.
If the bijector's domain is constrained, this proceeds by drawing an
unconstrained Tensor and then transforming it to fit. The constraints are
declared in `bijectors.hypothesis_testlib.bijector_supports`. The
transformations are defined by `tfp_hps.constrainer`.
Args:
draw: Hypothesis strategy sampler supplied by `@hps.composite`.
bijector: A `Bijector` in whose domain the Tensors will be.
shape: An optional `TensorShape`. The shape of the resulting
Tensors. Hypothesis will pick one if omitted.
Returns:
tensors: A strategy for drawing domain Tensors for the desired bijector.
"""
if is_invert(bijector):
return draw(codomain_tensors(bijector.bijector, shape))
if shape is None:
shape = draw(tfp_hps.shapes())
bijector_name = type(bijector).__name__
support = bijector_hps.bijector_supports()[bijector_name].forward
if isinstance(bijector, tfb.PowerTransform):
constraint_fn = bijector_hps.power_transform_constraint(bijector.power)
else:
constraint_fn = tfp_hps.constrainer(support)
return draw(tfp_hps.constrained_tensors(constraint_fn, shape))
def codomain_tensors(draw, bijector, shape=None):
if is_invert(bijector):
return draw(domain_tensors(bijector.bijector, shape))
if shape is None:
shape = draw(tfp_hps.batch_shapes())
bijector_name = type(bijector).__name__
support = bijector_hps.bijector_supports()[bijector_name].inverse
constraint_fn = constrainer(support)
return draw(tfp_hps.constrained_tensors(constraint_fn, shape))
def testDistribution(self, data):
enable_vars = data.draw(hps.booleans())
# TODO(b/146572907): Fix `enable_vars` for metadistributions.
broken_dists = EVENT_SPACE_BIJECTOR_IS_BROKEN
if enable_vars:
broken_dists.extend(dhps.INSTANTIABLE_META_DISTS)
dist = data.draw(
dhps.distributions(
enable_vars=enable_vars,
eligibility_filter=(lambda name: name not in broken_dists)))
self.evaluate([var.initializer for var in dist.variables])
self.check_bad_loc_scale(dist)
event_space_bijector = dist._experimental_default_event_space_bijector(
)
if event_space_bijector is None:
return
total_sample_shape = tensorshape_util.concatenate(
# Draw a sample shape
data.draw(tfp_hps.shapes()),
# Draw a shape that broadcasts with `[batch_shape, inverse_event_shape]`
# where `inverse_event_shape` is the event shape in the bijector's
# domain. This is the shape of `y` in R**n, such that
# x = event_space_bijector(y) has the event shape of the distribution.
data.draw(
tfp_hps.broadcasting_shapes(tensorshape_util.concatenate(
dist.batch_shape,
event_space_bijector.inverse_event_shape(
dist.event_shape)),
n=1))[0])
y = data.draw(
tfp_hps.constrained_tensors(tfp_hps.identity_fn,
total_sample_shape.as_list()))
x = event_space_bijector(y)
with tf.control_dependencies(dist._sample_control_dependencies(x)):
self.evaluate(tf.identity(x))
def check_event_space_bijector_constrains(self, dist, data):
event_space_bijector = dist.experimental_default_event_space_bijector()
if event_space_bijector is None:
return
# Draw a sample shape
sample_shape = data.draw(tfp_hps.shapes())
inv_event_shape = event_space_bijector.inverse_event_shape(
tensorshape_util.concatenate(dist.batch_shape, dist.event_shape))
# Draw a shape that broadcasts with `[batch_shape, inverse_event_shape]`
# where `inverse_event_shape` is the event shape in the bijector's
# domain. This is the shape of `y` in R**n, such that
# x = event_space_bijector(y) has the event shape of the distribution.
# TODO(b/174778703): Actually draw broadcast compatible shapes.
batch_inv_event_compat_shape = inv_event_shape
# batch_inv_event_compat_shape = data.draw(
# tfp_hps.broadcast_compatible_shape(inv_event_shape))
# batch_inv_event_compat_shape = tensorshape_util.concatenate(
# (1,) * (len(inv_event_shape) - len(batch_inv_event_compat_shape)),
# batch_inv_event_compat_shape)
total_sample_shape = tensorshape_util.concatenate(
sample_shape, batch_inv_event_compat_shape)
# full_sample_batch_event_shape = tensorshape_util.concatenate(
# sample_shape, inv_event_shape)
y = data.draw(
tfp_hps.constrained_tensors(tfp_hps.identity_fn,
total_sample_shape.as_list()))
hp.note('Trying to constrain inputs {}'.format(y))
with tfp_hps.no_tf_rank_errors():
x = event_space_bijector(y)
hp.note('Got constrained samples {}'.format(x))
with tf.control_dependencies(dist._sample_control_dependencies(x)):
self.evaluate(tensor_util.identity_as_tensor(x))
