tf.contrib.distributions.TransformedDistribution

class tf.contrib.distributions.TransformedDistribution

See the guide: Statistical Distributions (contrib) > Transformed distributions

A Transformed Distribution.

A TransformedDistribution models p(y) given a base distribution p(x), and a deterministic, invertible, differentiable transform, Y = g(X). The transform is typically an instance of the Bijector class and the base distribution is typically an instance of the Distribution class.

A Bijector is expected to implement the following functions: - forward, - inverse, - inverse_log_det_jacobian. The semantics of these functions are outlined in the Bijector documentation.

We now describe how a TransformedDistribution alters the input/outputs of a Distribution associated with a random variable (rv) X.

Write cdf(Y=y) for an absolutely continuous cumulative distribution function of random variable Y; write the probability density function pdf(Y=y) := d^k / (dy_1,...,dy_k) cdf(Y=y) for its derivative wrt to Y evaluated at y. Assume that Y = g(X) where g is a deterministic diffeomorphism, i.e., a non-random, continuous, differentiable, and invertible function. Write the inverse of g as X = g^{-1}(Y) and (J o g)(x) for the Jacobian of g evaluated at x.

A TransformedDistribution implements the following operations:

  • sample:

    Mathematically:

    Y = g(X)
    

    Programmatically:

    return bijector.forward(distribution.sample(...))
    
  • log_prob:

    Mathematically:

    (log o pdf)(Y=y) = (log o pdf o g^{-1})(y) +
                         (log o abs o det o J o g^{-1})(y)
    

    Programmatically:

    return (distribution.log_prob(bijector.inverse(x)) +
            bijector.inverse_log_det_jacobian(x))
    
  • log_cdf:

    Mathematically:

    (log o cdf)(Y=y) = (log o cdf o g^{-1})(y)
    

    Programmatically:

    return distribution.log_cdf(bijector.inverse(x))
    
  • and similarly for: cdf, prob, log_survival_function, survival_function.

A simple example constructing a Log-Normal distribution from a Normal distribution:

ds = tf.contrib.distributions
log_normal = ds.TransformedDistribution(
  distribution=ds.Normal(mu=mu, sigma=sigma),
  bijector=ds.bijector.Exp(),
  name="LogNormalTransformedDistribution")

A LogNormal made from callables:

ds = tf.contrib.distributions
log_normal = ds.TransformedDistribution(
  distribution=ds.Normal(mu=mu, sigma=sigma),
  bijector=ds.bijector.Inline(
    forward_fn=tf.exp,
    inverse_fn=tf.log,
    inverse_log_det_jacobian_fn=(
      lambda y: -tf.reduce_sum(tf.log(y), reduction_indices=-1)),
  name="LogNormalTransformedDistribution")

Another example constructing a Normal from a StandardNormal:

ds = tf.contrib.distributions
normal = ds.TransformedDistribution(
  distribution=ds.Normal(mu=0, sigma=1),
  bijector=ds.bijector.ScaleAndShift(loc=mu, scale=sigma, event_ndims=0),
  name="NormalTransformedDistribution")

A TransformedDistribution's batch- and event-shape are implied by the base distribution unless explicitly overridden by batch_shape or event_shape arguments. Specifying an overriding batch_shape (event_shape) is permitted only if the base distribution has scalar batch-shape (event-shape). The bijector is applied to the distribution as if the distribution possessed the overridden shape(s). The following example demonstrates how to construct a multivariate Normal as a TransformedDistribution.

bs = tf.contrib.distributions.bijector
ds = tf.contrib.distributions
# We will create two MVNs with batch_shape = event_shape = 2.
mean = [[-1., 0],      # batch:0
        [0., 1]]       # batch:1
chol_cov = [[[1., 0],
             [0, 1]],  # batch:0
            [[1, 0],
             [2, 2]]]  # batch:1
mvn1 = ds.TransformedDistribution(
    distribution=ds.Normal(mu=0., sigma=1.),
    bijector=bs.Affine(shift=mean, tril=chol_cov),
    batch_shape=[2],  # Valid because base_distribution.batch_shape == [].
    event_shape=[2])  # Valid because base_distribution.event_shape == [].
mvn2 = ds.MultivariateNormalCholesky(mu=mean, chol=chol_cov)
# mvn1.log_prob(x) == mvn2.log_prob(x)

Properties

allow_nan_stats

Python boolean describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)^2] is also undefined.

Returns:

  • allow_nan_stats: Python boolean.

bijector

Function transforming x => y.

distribution

Base distribution, p(x).

dtype

The DType of Tensors handled by this Distribution.

is_continuous

is_reparameterized

name

Name prepended to all ops created by this Distribution.

parameters

Dictionary of parameters used to instantiate this Distribution.

validate_args

Python boolean indicated possibly expensive checks are enabled.

Methods

__init__(distribution, bijector=None, batch_shape=None, event_shape=None, validate_args=False, name=None)

Construct a Transformed Distribution.

Args:

  • distribution: The base distribution instance to transform. Typically an instance of Distribution.
  • bijector: The object responsible for calculating the transformation. Typically an instance of Bijector. None means Identity().
  • batch_shape: integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
  • event_shape: integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
  • validate_args: Python Boolean. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
  • name: The name for the distribution. Default: bijector.name + distribution.name.

batch_shape(name='batch_shape')

Shape of a single sample from a single event index as a 1-D Tensor.

The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents.

Args:

  • name: name to give to the op

Returns:

  • batch_shape: Tensor.

cdf(value, name='cdf', **condition_kwargs)

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]

Additional documentation from TransformedDistribution:

condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

copy(**override_parameters_kwargs)

Creates a deep copy of the distribution.

Args:

**override_parameters_kwargs: String/value dictionary of initialization arguments to override with new values.

Returns:

  • distribution: A new instance of type(self) intitialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

entropy(name='entropy')

Shannon entropy in nats.

event_shape(name='event_shape')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:

  • name: name to give to the op

Returns:

  • event_shape: Tensor.

get_batch_shape()

Shape of a single sample from a single event index as a TensorShape.

Same meaning as batch_shape. May be only partially defined.

Returns:

  • batch_shape: TensorShape, possibly unknown.

get_event_shape()

Shape of a single sample from a single batch as a TensorShape.

Same meaning as event_shape. May be only partially defined.

Returns:

  • event_shape: TensorShape, possibly unknown.

is_scalar_batch(name='is_scalar_batch')

Indicates that batch_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_batch: Boolean scalar Tensor.

is_scalar_event(name='is_scalar_event')

Indicates that event_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_event: Boolean scalar Tensor.

log_cdf(value, name='log_cdf', **condition_kwargs)

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Additional documentation from TransformedDistribution:

condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_pdf(value, name='log_pdf', **condition_kwargs)

Log probability density function.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

Raises:

  • TypeError: if not is_continuous.

log_pmf(value, name='log_pmf', **condition_kwargs)

Log probability mass function.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

Raises:

  • TypeError: if is_continuous.

log_prob(value, name='log_prob', **condition_kwargs)

Log probability density/mass function (depending on is_continuous).

Additional documentation from TransformedDistribution:

Implements (log o p o g^{-1})(y) + (log o abs o det o J o g^{-1})(y), where g^{-1} is the inverse of transform.

  Also raises a `ValueError` if `inverse` was not provided to the
  distribution and `y` was not returned from `sample`.
condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_survival_function(value, name='log_survival_function', **condition_kwargs)

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Additional documentation from TransformedDistribution:

condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

mean(name='mean')

Mean.

mode(name='mode')

Mode.

param_shapes(cls, sample_shape, name='DistributionParamShapes')

Shapes of parameters given the desired shape of a call to sample().

Subclasses should override static method _param_shapes.

Args:

  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.

Returns:

dict of parameter name to Tensor shapes.

param_static_shapes(cls, sample_shape)

param_shapes with static (i.e. TensorShape) shapes.

Args:

  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().

Returns:

dict of parameter name to TensorShape.

Raises:

  • ValueError: if sample_shape is a TensorShape and is not fully defined.

pdf(value, name='pdf', **condition_kwargs)

Probability density function.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

Raises:

  • TypeError: if not is_continuous.

pmf(value, name='pmf', **condition_kwargs)

Probability mass function.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

Raises:

  • TypeError: if is_continuous.

prob(value, name='prob', **condition_kwargs)

Probability density/mass function (depending on is_continuous).

Additional documentation from TransformedDistribution:

Implements p(g^{-1}(y)) det|J(g^{-1}(y))|, where g^{-1} is the inverse of transform.

  Also raises a `ValueError` if `inverse` was not provided to the
  distribution and `y` was not returned from `sample`.
condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

sample(sample_shape=(), seed=None, name='sample', **condition_kwargs)

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:

  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer seed for RNG
  • name: name to give to the op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • samples: a Tensor with prepended dimensions sample_shape.

std(name='std')

Standard deviation.

survival_function(value, name='survival_function', **condition_kwargs)

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).

Additional documentation from TransformedDistribution:

condition_kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

Args:

  • value: float or double Tensor.
  • name: The name to give this op. **condition_kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

variance(name='variance')

Variance.

Defined in tensorflow/contrib/distributions/python/ops/transformed_distribution.py.