tfp.experimental.distributions.JointDistributionPinned | TensorFlow Probability (original) (raw)

A wrapper class for JointDistribution which pins, e.g., the evidence.

tfp.experimental.distributions.JointDistributionPinned(
    distribution, *pins, name=None, **named_pins
)

This object is experimental; the API may change without warning.

Think of this object as functools.partial for joint distributions. Sampling trims off pinned values (after specifying them as jd.sample(value=pins) to the underlying distribution). Log-density evaluates the joint probability of the given event and the pinned values.

Mathematically speaking, the object represents a joint probability density,p(x, y) where the x are pinned and the y are unpinned. Accordingly, it is also proportional to p(y | x), up to a (generally) intractable normalizing constant p(x), i.e. p(x, y) = p(y | x) p(x).

The measure on the unpinned values y that this object represents is no longer a probability measure, in that the integral oflog_prob over the space of y is p(x), which in general is not

As such, this is not a tfp.distributions.Distribution, and lacks a sample method. In its place, it provides:

sample_unpinned, sample_and_log_weight

A common use-case with probabilistic inference is writing out a generative model to explain some observed data:

jd = tfd.JointDistributionNamed(dict(
  loc = tfd.Normal(0., 1.),
  scale = tfd.Gamma(1., 1.),
  obs = lambda loc, scale: tfd.Normal(loc, scale),
))

Later, when we want to infer 'typical' values of loc and scale conditioned on some given data, we will often write:

def target_log_prob_fn(loc, scale):
  return jd.log_prob(loc=loc, scale=scale, obs=data)

This class enables one to write instead:

partial = tfde.JointDistributionPinned(jd, obs=data)
target_log_prob_fn = partial.log_prob

Or, even more concisely partial = jd.experimental_pin(obs=data).

This is nice, but it wasn't too hard to write out the target_log_prob_fnfunction explicitly.

Now, let's consider that for many inference and optimization methods, we may want to use a smooth change of variables to perform inference in the unconstrained space of real numbers. In some cases this transformation can be parameter-dependent. For example, if we want to unconstrain the support oftfp.distributions.Uniform(-3., 2.) to the real line, we might usetfp.bijectors.Sigmoid(low=-3., high=2.). In support of such use cases, most distributions (including the JointDistribution* classes) provide aexperimental_default_event_space_bijector() method.

When these transformations may be dependent on ancestral parts of a joint distribution, and some of those parameters may be pinned, it is helpful to have a utility class to bridge the gap and provide the multi-part bijective transform. This is the "raison d'être" of this class.

The model below is somewhat contrived, but demonstrates the use-case.

tfd = tfp.distributions
tfde = tfp.experimental.distributions

n = 75
dim = 3
joint = tfd.JointDistributionNamed(dict(
  upper = tfd.Uniform(.4, 1.5),
  concentration = tfd.Gamma(1., .5),
  corr = lambda concentration: tfd.CholeskyLKJ(
      dim, concentration=concentration),
  stddev = lambda upper: tfd.Sample(tfd.Uniform(.2, upper), dim),
  obs = lambda corr, stddev: tfd.Sample(
      tfd.MultivariateNormalTriL(
          loc=tf.zeros([dim]), scale_tril=corr * stddev[..., tf.newaxis]),
      n)
))
fixed_upper = 1.3
data = joint.sample(upper=fixed_upper)['obs']

pinned = tfde.JointDistributionPinned(joint, upper=fixed_upper, obs=data)
bij = pinned.experimental_default_event_space_bijector()
pulled_back_shape = bij.inverse_event_shape(pinned.event_shape)

# Fit an ensemble using SGD.
batch = 16
uniform_init = tf.nest.map_structure(
    lambda s: tf.random.uniform(tf.concat([[batch], s], axis=0), -2., 2.),
    pulled_back_shape)
vars = tf.nest.map_structure(tf.Variable, uniform_init)

opt = tf.optimizers.Adam(.01)

@tf.function(autograph=False)
def one_step():
  with tf.GradientTape() as tape:
    lp = pinned.log_prob(bij.forward(vars))
  gradients = tape.gradient(lp, vars)
  opt.apply_gradients(zip(gradients.values(), vars.values()))

for _ in range(100):
  one_step()

# Alternatively, sample using MCMC (currently aspirational):
initial_state = bij.forward(uniform_init)

kernel = tfp.mcmc.HamiltonianMonteCarlo(
    target_log_prob_fn=pinned.log_prob,
    step_size=.5, num_leapfrog_steps=4)
# **This line is currently aspirational**, to demonstrate the use-case.
kernel = tfp.mcmc.TransformedTransitionKernel(kernel, bij)
tfp.mcmc.sample_chain(10, kernel=kernel, current_state=initial_state)

Args
distribution	A tfp.distributions.JointDistribution.
*pins	A single object like the value argument that may be passed intoJointDistribution.sample (some parts may be None), or a sequence of objects similar to such sequence as might be passed toJointDistribution.log_prob, but with the difference that some parts may be None (log_prob would require all parts be specified). More precisely, the user may pass (A) a single argument specifiying pins of one or more of the parts of the underlying distribution either by name (i.e. a dict, namedtuple) or by sequence ordering (tuple,list), or (B) a sequence of arguments which align with the model of the underlying distribution (which must be ordered). It is an error to use an unordered sequence of pins with an unordered model, e.g. atfp.distributions.JointDistributionNamed constructed with a dictmodel (collections.OrderedDict is allowed).
name	Python str name for this distribution. If None, defaults to 'Pinned{distribution.name}'. Default value: None.
**named_pins	Named elements to pin. The names given must align with the part names defined by distribution._flat_resolve_names(), i.e. either the explicitly named parts of tfp.distributions.JointDistributionNamedor the name parameters passed to distributions constructed by the model given to JointDistribution*.

Attributes
batch_shape
distribution	The underlying distribution being partially pinned.
dtype	DType of unpinned parts.
event_shape	Statically resolvable event shapes of unpinned parts.
experimental_shard_axis_names
name	Name of this pinned distribution.
pins	Dictionary of pins resolved to names.
reparameterization_type
use_vectorized_map	Whether the underlying distribution relies on automatic vectorization.
validate_args

Methods

`batch_shape_tensor`

View source

batch_shape_tensor()

`event_shape_tensor`

View source

event_shape_tensor()

Dynamic/graph Tensor event shapes of unpinned parts.

`experimental_default_event_space_bijector`

View source

experimental_default_event_space_bijector(
    *args, **kwargs
)

A bijector to pull back unpinned values to unconstrained reals.

`experimental_pin`

View source

experimental_pin(
    *args, **kwargs
)

Logical equivalent of JointDistribution.experimental_pin.

For example

@tfd.JointDistributionCoroutine
def model():
    x = yield tfd.Normal(0, 1, name='x'),
    y = yield tfd.Normal(0, 1, name='y'),
    yield tfd.Normal(0, 1, name='z')
model.experimental_pin(z=1.).experimental_pin(y=.5).event_shape
# => StructTuple(x=[])

Args
*args	Positional arguments: a value structure or component values.
**kwargs	Keyword arguments: a value structure or component values. May also include name, specifying a Python string name for ops generated by this method.

Returns
pinned	a tfp.experimental.distributions.JointDistributionPinned with the given values pinned in addition to those pins already specified onself.

`experimental_sample_and_log_prob`

View source

experimental_sample_and_log_prob(
    sample_shape=(), seed=None, name='sample_and_log_prob', **kwargs
)

`log_prob`

View source

log_prob(
    *args, **kwargs
)

Computes the log-density of this measure at the given point.

The measure on the space of unpinned values that is represented by this object is not a probability measure, so the values oflog_prob will in general not integrate to 1.

The methods of JointDistributionPinned (unnormalized_log_prob,sample_and_log_weight, etc.) can be called by passing a single structure of tensors, a sequence of tensor arguments, or using named args for each part. For example:

tfde = tfp.experimental.distributions

# Given the following joint distribution:
jd = tfd.JointDistributionSequential([
    tfd.Normal(0., 1., name='z'),
    tfd.Normal(0., 1., name='y'),
    lambda y, z: tfd.Normal(y + z, 1., name='x')
], validate_args=True)

# The following `__init__` styles are all permissible and produce
# `JointDistributionPinned` objects behaving identically.
PartialXY = collections.namedtuple('PartialXY', 'x,y')
PartialX = collections.namedtuple('PartialX', 'x')
OrderedDict = collections.OrderedDict
assert (tfde.JointDistributionPinned(jd, x=2.).pins ==
        tfde.JointDistributionPinned(jd, x=2., z=None).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialXY(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialX(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, None, None, 2.).pins ==
        tfde.JointDistributionPinned(jd, [None, None, 2.]).pins)
# (Notice that the `pins` attribute is always resolved to a `dict`.)

pinned = tfde.JointDistributionPinned(jd, x=2.)
pinned.dtype
# ==> [tf.float32, tf.float32]
z, y = sample = pinned.sample_unpinned()

# The following calling styles are all permissable and produce the exactly
# the same output.
PartialZY = collections.namedtuple('PartialZY', 'z,y')
assert (pinned.log_prob(sample) ==
        pinned.log_prob(z, y) ==
        pinned.log_prob(z=z, y=y) ==
        pinned.log_prob(PartialZY(z=z, y=y)))

# These calling possibilities also imply that one can also use `*`
# expansion, if `sample` is a sequence:
pinned.log_prob(*sample)
# and similarly, if `sample` is a map, one can use `**` expansion:
pinned.log_prob(**sample)

Component distributions' names are resolved via jd._flat_resolve_names(), which is implemented by each JointDistribution subclass (see subclass documentation for details). Generally, for components where a name was provided---either explicitly as the name argument to a distribution or as a key in a dict-valued JointDistribution, or implicitly, e.g., by the argument name of a JointDistributionSequential distribution-making function---the provided name will be used. Otherwise the component will receive a dummy name; these may change without warning and should not be relied upon.

In general, return types of part-wise methods/properties are determined by those of the underlying JointDistribution's model type:

StructTuple for JointDistributionCoroutine, and forJointDistributionNamed with namedtuple model type.
collections.OrderedDict for JointDistributionNamed with OrderedDictmodel type.
dict for JointDistributionNamed with dict model type.
tuple or list for JointDistributionSequential.

pinned = tfde.JointDistributionPinned(
    tfd.JointDistributionSequential(
        [tfd.Exponential(1.), lambda s: tfd.Normal(0., s)]),
        None, 1.2)
pinned.dtype  # => [tf.float32]
pinned.log_prob([4.])
# ==> Tensor with shape `[]`.
lp = pinned.log_prob(4.)
# ==> Tensor with shape `[]`.

Notice that in the first call, [4.] is interpreted as a list of one scalar while in the second call the input is a scalar. Hence both inputs result in identical scalar outputs. If we wanted to pass an explicit vector to the Exponential component---creating a vector-shaped batch of log_probs---we could instead writepinned.log_prob(np.array([4])).

Args
*args	Positional arguments: a value structure or component values (see above).
**kwargs	Keyword arguments: a value structure or component values (see above). May also include name, specifying a Python string name for ops generated by this method.

Returns
log_prob	The joint log-probability of xs or kwargswith the pinned parts. It is unnormalized with respect to xs or**kwargs.

`log_prob_parts`

View source

log_prob_parts(
    *args, **kwargs
)

Computes the log-probability of each part.

The measure on the space of unpinned values that is represented by this object is not a probability measure, so the values produced by log_prob_parts will in general not integrate to 1.

tfde = tfp.experimental.distributions

# Given the following joint distribution:
jd = tfd.JointDistributionSequential([
    tfd.Normal(0., 1., name='z'),
    tfd.Normal(0., 1., name='y'),
    lambda y, z: tfd.Normal(y + z, 1., name='x')
], validate_args=True)

# The following `__init__` styles are all permissible and produce
# `JointDistributionPinned` objects behaving identically.
PartialXY = collections.namedtuple('PartialXY', 'x,y')
PartialX = collections.namedtuple('PartialX', 'x')
OrderedDict = collections.OrderedDict
assert (tfde.JointDistributionPinned(jd, x=2.).pins ==
        tfde.JointDistributionPinned(jd, x=2., z=None).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialXY(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialX(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, None, None, 2.).pins ==
        tfde.JointDistributionPinned(jd, [None, None, 2.]).pins)
# (Notice that the `pins` attribute is always resolved to a `dict`.)

pinned = tfde.JointDistributionPinned(jd, x=2.)
pinned.dtype
# ==> [tf.float32, tf.float32]
z, y = sample = pinned.sample_unpinned()

# The following calling styles are all permissable and produce the exactly
# the same output.
PartialZY = collections.namedtuple('PartialZY', 'z,y')
assert (pinned.log_prob_parts(sample) ==
        pinned.log_prob_parts(z, y) ==
        pinned.log_prob_parts(z=z, y=y) ==
        pinned.log_prob_parts(PartialZY(z=z, y=y)))

# These calling possibilities also imply that one can also use `*`
# expansion, if `sample` is a sequence:
pinned.log_prob_parts(*sample)
# and similarly, if `sample` is a map, one can use `**` expansion:
pinned.log_prob_parts(**sample)

In general, return types of part-wise methods/properties are determined by those of the underlying JointDistribution's model type:

StructTuple for JointDistributionCoroutine, and forJointDistributionNamed with namedtuple model type.
collections.OrderedDict for JointDistributionNamed with OrderedDictmodel type.
dict for JointDistributionNamed with dict model type.
tuple or list for JointDistributionSequential.

pinned = tfde.JointDistributionPinned(
    tfd.JointDistributionSequential(
        [tfd.Exponential(1.), lambda s: tfd.Normal(0., s)]),
        None, 1.2)
pinned.dtype  # => [tf.float32]
pinned.log_prob_parts([4.])
# ==> Tensor with shape `[]`.
lp_parts = pinned.log_prob_parts(4.)
# ==> Tensor with shape `[]`.

Notice that in the first call, [4.] is interpreted as a list of one scalar while in the second call the input is a scalar. Hence both inputs result in identical scalar outputs. If we wanted to pass an explicit vector to the Exponential component---creating a vector-shaped batch of log_prob_partss---we could instead writepinned.log_prob_parts(np.array([4])).

Args
*args	Positional arguments: a value structure or component values (see above).
**kwargs	Keyword arguments: a value structure or component values (see above). May also include name, specifying a Python string name for ops generated by this method.

Returns
pinned	partial log-prob of each pinned part
unpinned	partial log-prob of each unpinned part

`log_weight`

View source

log_weight(
    *args, **kwargs
)

Computes the log relative weight of the given sample.

This function computes the log-probability of the pinned parts at the given location, ignoring the probability of the unpinned parts.

tfde = tfp.experimental.distributions

# Given the following joint distribution:
jd = tfd.JointDistributionSequential([
    tfd.Normal(0., 1., name='z'),
    tfd.Normal(0., 1., name='y'),
    lambda y, z: tfd.Normal(y + z, 1., name='x')
], validate_args=True)

# The following `__init__` styles are all permissible and produce
# `JointDistributionPinned` objects behaving identically.
PartialXY = collections.namedtuple('PartialXY', 'x,y')
PartialX = collections.namedtuple('PartialX', 'x')
OrderedDict = collections.OrderedDict
assert (tfde.JointDistributionPinned(jd, x=2.).pins ==
        tfde.JointDistributionPinned(jd, x=2., z=None).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialXY(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialX(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, None, None, 2.).pins ==
        tfde.JointDistributionPinned(jd, [None, None, 2.]).pins)
# (Notice that the `pins` attribute is always resolved to a `dict`.)

pinned = tfde.JointDistributionPinned(jd, x=2.)
pinned.dtype
# ==> [tf.float32, tf.float32]
z, y = sample = pinned.sample_unpinned()

# The following calling styles are all permissable and produce the exactly
# the same output.
PartialZY = collections.namedtuple('PartialZY', 'z,y')
assert (pinned.log_weight(sample) ==
        pinned.log_weight(z, y) ==
        pinned.log_weight(z=z, y=y) ==
        pinned.log_weight(PartialZY(z=z, y=y)))

# These calling possibilities also imply that one can also use `*`
# expansion, if `sample` is a sequence:
pinned.log_weight(*sample)
# and similarly, if `sample` is a map, one can use `**` expansion:
pinned.log_weight(**sample)

In general, return types of part-wise methods/properties are determined by those of the underlying JointDistribution's model type:

StructTuple for JointDistributionCoroutine, and forJointDistributionNamed with namedtuple model type.
collections.OrderedDict for JointDistributionNamed with OrderedDictmodel type.
dict for JointDistributionNamed with dict model type.
tuple or list for JointDistributionSequential.

pinned = tfde.JointDistributionPinned(
    tfd.JointDistributionSequential(
        [tfd.Exponential(1.), lambda s: tfd.Normal(0., s)]),
        None, 1.2)
pinned.dtype  # => [tf.float32]
pinned.log_weight([4.])
# ==> Tensor with shape `[]`.
log_wt = pinned.log_weight(4.)
# ==> Tensor with shape `[]`.

Notice that in the first call, [4.] is interpreted as a list of one scalar while in the second call the input is a scalar. Hence both inputs result in identical scalar outputs. If we wanted to pass an explicit vector to the Exponential component---creating a vector-shaped batch of log_weights---we could instead writepinned.log_weight(np.array([4])).

Args
*args	Positional arguments: a value structure or component values (see above).
**kwargs	Keyword arguments: a value structure or component values (see above). May also include name, specifying a Python string name for ops generated by this method.

Returns
log_weights	log-weight of the given point, i.e. the log pinned evidence.

`sample_and_log_weight`

View source

sample_and_log_weight(
    sample_shape=(), seed=None
)

Draws unnormalized samples and their log-weights with ancestral sampling.

This method provides the ancestral importance sampler for the non-probability measure on the unpinned space represented by this object. To wit, we draw a sample with the same distribution assample_unpinned, and return it together with a weight given by the log-probability of the pinned parts.

Denoting the unpinned space by Y, the samples by y, their corresponding log-weights by w, and the measure defined by this object by m, the spec for this method is that, for any real-valued function f : Y -> R,

int_Y f(y) dm = E [f(y) * exp(w)].

Args
sample_shape	Shape prefix to use when sampling.
seed	PRNG seed; see tfp.random.sanitize_seed for details.

Returns
samples	unpinned parts ancestrally sampled from the pinned distribution.
log_weights	log-weight of the sample. (Log-probability of the pinned parts at the sampled location.)

`sample_unpinned`

View source

sample_unpinned(
    sample_shape=(), seed=None
)

Draws unnormalized samples using ancestral sampling.

This produces the same distribution on outputs as callingunderlying.sample(value=pins), then stripping away the pinned parts.

In the probability literature, this operation corresponds to sampling from the do-calculus expressionunderlying(Unpinned|do(Pinned = pins)), where the assumed causal structure of underlying is given by its data dependence graph.

Note that this is not the same measure as is represented by thelog_prob method of this class, which is why this method is not called sample.

Args
sample_shape	Shape prefix to use when sampling.
seed	PRNG seed; see tfp.random.sanitize_seed for details.

Returns
samples	unpinned parts sampled from the underlying distribution.

`unnormalized_log_prob`

View source

unnormalized_log_prob(
    *args, **kwargs
)

Computes the log-density of this measure at the given point.

The measure on the space of unpinned values that is represented by this object is not a probability measure, so the values ofunnormalized_log_prob will in general not integrate to 1.

This method currently computes the same values as log_prob, but may be modified in the future to omit normalization constants.

tfde = tfp.experimental.distributions

# Given the following joint distribution:
jd = tfd.JointDistributionSequential([
    tfd.Normal(0., 1., name='z'),
    tfd.Normal(0., 1., name='y'),
    lambda y, z: tfd.Normal(y + z, 1., name='x')
], validate_args=True)

# The following `__init__` styles are all permissible and produce
# `JointDistributionPinned` objects behaving identically.
PartialXY = collections.namedtuple('PartialXY', 'x,y')
PartialX = collections.namedtuple('PartialX', 'x')
OrderedDict = collections.OrderedDict
assert (tfde.JointDistributionPinned(jd, x=2.).pins ==
        tfde.JointDistributionPinned(jd, x=2., z=None).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialXY(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialX(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, None, None, 2.).pins ==
        tfde.JointDistributionPinned(jd, [None, None, 2.]).pins)
# (Notice that the `pins` attribute is always resolved to a `dict`.)

pinned = tfde.JointDistributionPinned(jd, x=2.)
pinned.dtype
# ==> [tf.float32, tf.float32]
z, y = sample = pinned.sample_unpinned()

# The following calling styles are all permissable and produce the exactly
# the same output.
PartialZY = collections.namedtuple('PartialZY', 'z,y')
assert (pinned.unnormalized_log_prob(sample) ==
        pinned.unnormalized_log_prob(z, y) ==
        pinned.unnormalized_log_prob(z=z, y=y) ==
        pinned.unnormalized_log_prob(PartialZY(z=z, y=y)))

# These calling possibilities also imply that one can also use `*`
# expansion, if `sample` is a sequence:
pinned.unnormalized_log_prob(*sample)
# and similarly, if `sample` is a map, one can use `**` expansion:
pinned.unnormalized_log_prob(**sample)

In general, return types of part-wise methods/properties are determined by those of the underlying JointDistribution's model type:

StructTuple for JointDistributionCoroutine, and forJointDistributionNamed with namedtuple model type.
collections.OrderedDict for JointDistributionNamed with OrderedDictmodel type.
dict for JointDistributionNamed with dict model type.
tuple or list for JointDistributionSequential.

pinned = tfde.JointDistributionPinned(
    tfd.JointDistributionSequential(
        [tfd.Exponential(1.), lambda s: tfd.Normal(0., s)]),
        None, 1.2)
pinned.dtype  # => [tf.float32]
pinned.unnormalized_log_prob([4.])
# ==> Tensor with shape `[]`.
lp = pinned.unnormalized_log_prob(4.)
# ==> Tensor with shape `[]`.

Notice that in the first call, [4.] is interpreted as a list of one scalar while in the second call the input is a scalar. Hence both inputs result in identical scalar outputs. If we wanted to pass an explicit vector to the Exponential component---creating a vector-shaped batch of unnormalized_log_probs---we could instead writepinned.unnormalized_log_prob(np.array([4])).

Args
*args	Positional arguments: a value structure or component values (see above).
**kwargs	Keyword arguments: a value structure or component values (see above). May also include name, specifying a Python string name for ops generated by this method.

Returns
log_prob	The joint log-probability of xs or kwargswith the pinned parts. It is unnormalized with respect to xs or**kwargs.

`unnormalized_log_prob_parts`

View source

unnormalized_log_prob_parts(
    *args, **kwargs
)

Computes the log-probability of each part.

The measure on the space of unpinned values that is represented by this object is not a probability measure, so the values produced by unnormalized_log_prob_parts will in general not integrate to 1.

This method currently computes the same values aslog_prob_parts, but may be modified in the future to omit normalization constants.

tfde = tfp.experimental.distributions

# Given the following joint distribution:
jd = tfd.JointDistributionSequential([
    tfd.Normal(0., 1., name='z'),
    tfd.Normal(0., 1., name='y'),
    lambda y, z: tfd.Normal(y + z, 1., name='x')
], validate_args=True)

# The following `__init__` styles are all permissible and produce
# `JointDistributionPinned` objects behaving identically.
PartialXY = collections.namedtuple('PartialXY', 'x,y')
PartialX = collections.namedtuple('PartialX', 'x')
OrderedDict = collections.OrderedDict
assert (tfde.JointDistributionPinned(jd, x=2.).pins ==
        tfde.JointDistributionPinned(jd, x=2., z=None).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, dict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, OrderedDict(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialXY(x=2., y=None)).pins ==
        tfde.JointDistributionPinned(jd, PartialX(x=2.)).pins ==
        tfde.JointDistributionPinned(jd, None, None, 2.).pins ==
        tfde.JointDistributionPinned(jd, [None, None, 2.]).pins)
# (Notice that the `pins` attribute is always resolved to a `dict`.)

pinned = tfde.JointDistributionPinned(jd, x=2.)
pinned.dtype
# ==> [tf.float32, tf.float32]
z, y = sample = pinned.sample_unpinned()

# The following calling styles are all permissable and produce the exactly
# the same output.
PartialZY = collections.namedtuple('PartialZY', 'z,y')
assert (pinned.unnormalized_log_prob_parts(sample) ==
        pinned.unnormalized_log_prob_parts(z, y) ==
        pinned.unnormalized_log_prob_parts(z=z, y=y) ==
        pinned.unnormalized_log_prob_parts(PartialZY(z=z, y=y)))

# These calling possibilities also imply that one can also use `*`
# expansion, if `sample` is a sequence:
pinned.unnormalized_log_prob_parts(*sample)
# and similarly, if `sample` is a map, one can use `**` expansion:
pinned.unnormalized_log_prob_parts(**sample)

In general, return types of part-wise methods/properties are determined by those of the underlying JointDistribution's model type:

StructTuple for JointDistributionCoroutine, and forJointDistributionNamed with namedtuple model type.
collections.OrderedDict for JointDistributionNamed with OrderedDictmodel type.
dict for JointDistributionNamed with dict model type.
tuple or list for JointDistributionSequential.

pinned = tfde.JointDistributionPinned(
    tfd.JointDistributionSequential(
        [tfd.Exponential(1.), lambda s: tfd.Normal(0., s)]),
        None, 1.2)
pinned.dtype  # => [tf.float32]
pinned.unnormalized_log_prob_parts([4.])
# ==> Tensor with shape `[]`.
lp_parts = pinned.unnormalized_log_prob_parts(4.)
# ==> Tensor with shape `[]`.

Notice that in the first call, [4.] is interpreted as a list of one scalar while in the second call the input is a scalar. Hence both inputs result in identical scalar outputs. If we wanted to pass an explicit vector to the Exponential component---creating a vector-shaped batch of unnormalized_log_prob_partss---we could instead writepinned.unnormalized_log_prob_parts(np.array([4])).

Args
*args	Positional arguments: a value structure or component values (see above).
**kwargs	Keyword arguments: a value structure or component values (see above). May also include name, specifying a Python string name for ops generated by this method.

Returns
pinned	partial log-prob of each pinned part
unpinned	partial log-prob of each unpinned part

`unpin`

View source

unpin(
    *pinned_part_names
)

Unpins selected parts, returning a new instance.

Args
*pinned_part_names	One or more str names of parts to unpin.

Returns
jd	A joint distribution with the specified pins dropped. If all pins are dropped, the underlying joint distribution is returned.