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A generic probability distribution base class.
tf.compat.v1.distributions.Distribution(
dtype, reparameterization_type, validate_args, allow_nan_stats, parameters=None,
graph_parents=None, name=None
)
Distribution is a base class for constructing and organizing properties
(e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian).
Subclasses are expected to implement a leading-underscore version of the
same-named function. The argument signature should be identical except for
the omission of name="...". For example, to enable log_prob(value,
name="log_prob") a subclass should implement _log_prob(value).
Subclasses can append to public-level docstrings by providing docstrings for their method specializations. For example:
@util.AppendDocstring("Some other details.")
def _log_prob(self, value):
...
would add the string "Some other details." to the log_prob function
docstring. This is implemented as a simple decorator to avoid python
linter complaining about missing Args/Returns/Raises sections in the
partial docstrings.
All distributions support batches of independent distributions of that type. The batch shape is determined by broadcasting together the parameters.
The shape of arguments to __init__, cdf, log_cdf, prob, and
log_prob reflect this broadcasting, as does the return value of sample and
sample_n.
sample_n_shape = [n] + batch_shape + event_shape, where sample_n_shape is
the shape of the Tensor returned from sample_n, n is the number of
samples, batch_shape defines how many independent distributions there are,
and event_shape defines the shape of samples from each of those independent
distributions. Samples are independent along the batch_shape dimensions, but
not necessarily so along the event_shape dimensions (depending on the
particulars of the underlying distribution).
Using the Uniform distribution as an example:
minval = 3.0
maxval = [[4.0, 6.0],
[10.0, 12.0]]
# Broadcasting:
# This instance represents 4 Uniform distributions. Each has a lower bound at
# 3.0 as the `minval` parameter was broadcasted to match `maxval`'s shape.
u = Uniform(minval, maxval)
# `event_shape` is `TensorShape([])`.
event_shape = u.event_shape
# `event_shape_t` is a `Tensor` which will evaluate to [].
event_shape_t = u.event_shape_tensor()
# Sampling returns a sample per distribution. `samples` has shape
# [5, 2, 2], which is [n] + batch_shape + event_shape, where n=5,
# batch_shape=[2, 2], and event_shape=[].
samples = u.sample_n(5)
# The broadcasting holds across methods. Here we use `cdf` as an example. The
# same holds for `log_cdf` and the likelihood functions.
# `cum_prob` has shape [2, 2] as the `value` argument was broadcasted to the
# shape of the `Uniform` instance.
cum_prob_broadcast = u.cdf(4.0)
# `cum_prob`'s shape is [2, 2], one per distribution. No broadcasting
# occurred.
cum_prob_per_dist = u.cdf([[4.0, 5.0],
[6.0, 7.0]])
# INVALID as the `value` argument is not broadcastable to the distribution's
# shape.
cum_prob_invalid = u.cdf([4.0, 5.0, 6.0])
There are three important concepts associated with TensorFlow Distributions
shapes:
- Event shape describes the shape of a single draw from the distribution;
it may be dependent across dimensions. For scalar distributions, the event
shape is []. For a 5-dimensional MultivariateNormal, the event shape is
[5].
- Batch shape describes independent, not identically distributed draws, aka a
"collection" or "bunch" of distributions.
- Sample shape describes independent, identically distributed draws of batches
from the distribution family.
The event shape and the batch shape are properties of a Distribution object,
whereas the sample shape is associated with a specific call to sample or
log_prob.
For detailed usage examples of TensorFlow Distributions shapes, see this tutorial
Some distributions do not have well-defined statistics for all initialization
parameter values. For example, the beta distribution is parameterized by
positive real numbers concentration1 and concentration0, and does not have
well-defined mode if concentration1 < 1 or concentration0 < 1.
The user is given the option of raising an exception or returning NaN.
a = tf.exp(tf.matmul(logits, weights_a))
b = tf.exp(tf.matmul(logits, weights_b))
# Will raise exception if ANY batch member has a < 1 or b < 1.
dist = distributions.beta(a, b, allow_nan_stats=False)
mode = dist.mode().eval()
# Will return NaN for batch members with either a < 1 or b < 1.
dist = distributions.beta(a, b, allow_nan_stats=True) # Default behavior
mode = dist.mode().eval()
In all cases, an exception is raised if invalid parameters are passed, e.g.
# Will raise an exception if any Op is run.
negative_a = -1.0 * a # beta distribution by definition has a > 0.
dist = distributions.beta(negative_a, b, allow_nan_stats=True)
dist.mean().eval()
dtype: The type of the event samples. None implies no type-enforcement.reparameterization_type: Instance of ReparameterizationType.
If distributions.FULLY_REPARAMETERIZED, this
Distribution can be reparameterized in terms of some standard
distribution with a function whose Jacobian is constant for the support
of the standard distribution. If distributions.NOT_REPARAMETERIZED,
then no such reparameterization is available.validate_args: Python bool, default False. When True distribution
parameters are checked for validity despite possibly degrading runtime
performance. When False invalid inputs may silently render incorrect
outputs.allow_nan_stats: Python bool, default True. When True, statistics
(e.g., mean, mode, variance) use the value "NaN" to indicate the
result is undefined. When False, an exception is raised if one or
more of the statistic's batch members are undefined.parameters: Python dict of parameters used to instantiate this
Distribution.graph_parents: Python list of graph prerequisites of this
Distribution.name: Python str name prefixed to Ops created by this class. Default:
subclass name.allow_nan_stats: Python bool 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.
batch_shape: Shape of a single sample from a single event index as a TensorShape.
May be partially defined or unknown.
The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.
dtype: The DType of Tensors handled by this Distribution.
event_shape: Shape of a single sample from a single batch as a TensorShape.
May be partially defined or unknown.
name: Name prepended to all ops created by this Distribution.
parameters: Dictionary of parameters used to instantiate this Distribution.
reparameterization_type: Describes how samples from the distribution are reparameterized.
Currently this is one of the static instances
distributions.FULLY_REPARAMETERIZED
or distributions.NOT_REPARAMETERIZED.
validate_args: Python bool indicating possibly expensive checks are enabled.
ValueError: if any member of graph_parents is None or not a Tensor.batch_shape_tensorbatch_shape_tensor(
name='batch_shape_tensor'
)
Shape of a single sample from a single event index as a 1-D Tensor.
The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.
name: name to give to the opbatch_shape: Tensor.cdfcdf(
value, name='cdf'
)
Cumulative distribution function.
Given random variable X, the cumulative distribution function cdf is:
cdf(x) := P[X <= x]
value: float or double Tensor.name: Python str prepended to names of ops created by this function.cdf: a Tensor of shape sample_shape(x) + self.batch_shape with
values of type self.dtype.copycopy(
**override_parameters_kwargs
)
Creates a deep copy of the distribution.
Note: the copy distribution may continue to depend on the original initialization arguments.
**override_parameters_kwargs: String/value dictionary of initialization
arguments to override with new values.distribution: A new instance of type(self) initialized from the union
of self.parameters and override_parameters_kwargs, i.e.,
dict(self.parameters, **override_parameters_kwargs).covariancecovariance(
name='covariance'
)
Covariance.
Covariance is (possibly) defined only for non-scalar-event distributions.
For example, for a length-k, vector-valued distribution, it is calculated
as,
Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]
where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E
denotes expectation.
Alternatively, for non-vector, multivariate distributions (e.g.,
matrix-valued, Wishart), Covariance shall return a (batch of) matrices
under some vectorization of the events, i.e.,
Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]
where Cov is a (batch of) k' x k' matrices,
0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function
mapping indices of this distribution's event dimensions to indices of a
length-k' vector.
name: Python str prepended to names of ops created by this function.covariance: Floating-point Tensor with shape [B1, ..., Bn, k', k']
where the first n dimensions are batch coordinates and
k' = reduce_prod(self.event_shape).cross_entropycross_entropy(
other, name='cross_entropy'
)
Computes the (Shannon) cross entropy.
Denote this distribution (self) by P and the other distribution by
Q. Assuming P, Q are absolutely continuous with respect to
one another and permit densities p(x) dr(x) and q(x) dr(x), (Shanon)
cross entropy is defined as:
H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)
where F denotes the support of the random variable X ~ P.
other: tfp.distributions.Distribution instance.name: Python str prepended to names of ops created by this function.cross_entropy: self.dtype Tensor with shape [B1, ..., Bn]
representing n different calculations of (Shanon) cross entropy.entropyentropy(
name='entropy'
)
Shannon entropy in nats.
event_shape_tensorevent_shape_tensor(
name='event_shape_tensor'
)
Shape of a single sample from a single batch as a 1-D int32 Tensor.
name: name to give to the opevent_shape: Tensor.is_scalar_batchis_scalar_batch(
name='is_scalar_batch'
)
Indicates that batch_shape == [].
name: Python str prepended to names of ops created by this function.is_scalar_batch: bool scalar Tensor.is_scalar_eventis_scalar_event(
name='is_scalar_event'
)
Indicates that event_shape == [].
name: Python str prepended to names of ops created by this function.is_scalar_event: bool scalar Tensor.kl_divergencekl_divergence(
other, name='kl_divergence'
)
Computes the Kullback--Leibler divergence.
Denote this distribution (self) by p and the other distribution by
q. Assuming p, q are absolutely continuous with respect to reference
measure r, the KL divergence is defined as:
KL[p, q] = E_p[log(p(X)/q(X))]
= -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
= H[p, q] - H[p]
where F denotes the support of the random variable X ~ p, H[., .]
denotes (Shanon) cross entropy, and H[.] denotes (Shanon) entropy.
other: tfp.distributions.Distribution instance.name: Python str prepended to names of ops created by this function.kl_divergence: self.dtype Tensor with shape [B1, ..., Bn]
representing n different calculations of the Kullback-Leibler
divergence.log_cdflog_cdf(
value, name='log_cdf'
)
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.
value: float or double Tensor.name: Python str prepended to names of ops created by this function.logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with
values of type self.dtype.log_problog_prob(
value, name='log_prob'
)
Log probability density/mass function.
value: float or double Tensor.name: Python str prepended to names of ops created by this function.log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with
values of type self.dtype.log_survival_functionlog_survival_function(
value, name='log_survival_function'
)
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.
value: float or double Tensor.name: Python str prepended to names of ops created by this function.Tensor of shape sample_shape(x) + self.batch_shape with values of type
self.dtype.
meanmean(
name='mean'
)
Mean.
modemode(
name='mode'
)
Mode.
param_shapes@classmethod
param_shapes(
sample_shape, name='DistributionParamShapes'
)
Shapes of parameters given the desired shape of a call to sample().
This is a class method that describes what key/value arguments are required
to instantiate the given Distribution so that a particular shape is
returned for that instance's call to sample().
Subclasses should override class method _param_shapes.
sample_shape: Tensor or python list/tuple. Desired shape of a call to
sample().name: name to prepend ops with.dict of parameter name to Tensor shapes.
param_static_shapes@classmethod
param_static_shapes(
sample_shape
)
param_shapes with static (i.e. TensorShape) shapes.
This is a class method that describes what key/value arguments are required
to instantiate the given Distribution so that a particular shape is
returned for that instance's call to sample(). Assumes that the sample's
shape is known statically.
Subclasses should override class method _param_shapes to return
constant-valued tensors when constant values are fed.
sample_shape: TensorShape or python list/tuple. Desired shape of a call
to sample().dict of parameter name to TensorShape.
ValueError: if sample_shape is a TensorShape and is not fully defined.probprob(
value, name='prob'
)
Probability density/mass function.
value: float or double Tensor.name: Python str prepended to names of ops created by this function.prob: a Tensor of shape sample_shape(x) + self.batch_shape with
values of type self.dtype.quantilequantile(
value, name='quantile'
)
Quantile function. Aka "inverse cdf" or "percent point function".
Given random variable X and p in [0, 1], the quantile is:
quantile(p) := x such that P[X <= x] == p
value: float or double Tensor.name: Python str prepended to names of ops created by this function.quantile: a Tensor of shape sample_shape(x) + self.batch_shape with
values of type self.dtype.samplesample(
sample_shape=(), seed=None, name='sample'
)
Generate samples of the specified shape.
Note that a call to sample() without arguments will generate a single
sample.
sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.seed: Python integer seed for RNGname: name to give to the op.samples: a Tensor with prepended dimensions sample_shape.stddevstddev(
name='stddev'
)
Standard deviation.
Standard deviation is defined as,
stddev = E[(X - E[X])**2]**0.5
where X is the random variable associated with this distribution, E
denotes expectation, and stddev.shape = batch_shape + event_shape.
name: Python str prepended to names of ops created by this function.stddev: Floating-point Tensor with shape identical to
batch_shape + event_shape, i.e., the same shape as self.mean().survival_functionsurvival_function(
value, name='survival_function'
)
Survival function.
Given random variable X, the survival function is defined:
survival_function(x) = P[X > x]
= 1 - P[X <= x]
= 1 - cdf(x).
value: float or double Tensor.name: Python str prepended to names of ops created by this function.Tensor of shape sample_shape(x) + self.batch_shape with values of type
self.dtype.
variancevariance(
name='variance'
)
Variance.
Variance is defined as,
Var = E[(X - E[X])**2]
where X is the random variable associated with this distribution, E
denotes expectation, and Var.shape = batch_shape + event_shape.
name: Python str prepended to names of ops created by this function.variance: Floating-point Tensor with shape identical to
batch_shape + event_shape, i.e., the same shape as self.mean().