Source code for pyro.distributions.distribution

from __future__ import absolute_import, division, print_function

from abc import ABCMeta, abstractmethod

from six import add_metaclass

from pyro.distributions.score_parts import ScoreParts

[docs]@add_metaclass(ABCMeta) class Distribution(object): """ Base class for parameterized probability distributions. Distributions in Pyro are stochastic function objects with :meth:`sample` and :meth:`log_prob` methods. Distribution are stochastic functions with fixed parameters:: d = dist.Bernoulli(param) x = d() # Draws a random sample. p = d.log_prob(x) # Evaluates log probability of x. **Implementing New Distributions**: Derived classes must implement the methods: :meth:`sample`, :meth:`log_prob`. **Examples**: Take a look at the `examples <>`_ to see how they interact with inference algorithms. """ has_rsample = False has_enumerate_support = False
[docs] def __call__(self, *args, **kwargs): """ Samples a random value (just an alias for ``.sample(*args, **kwargs)``). For tensor distributions, the returned tensor should have the same ``.shape`` as the parameters. :return: A random value. :rtype: torch.Tensor """ return self.sample(*args, **kwargs)
[docs] @abstractmethod def sample(self, *args, **kwargs): """ Samples a random value. For tensor distributions, the returned tensor should have the same ``.shape`` as the parameters, unless otherwise noted. :param sample_shape: the size of the iid batch to be drawn from the distribution. :type sample_shape: torch.Size :return: A random value or batch of random values (if parameters are batched). The shape of the result should be ``self.shape()``. :rtype: torch.Tensor """ raise NotImplementedError
[docs] @abstractmethod def log_prob(self, x, *args, **kwargs): """ Evaluates log probability densities for each of a batch of samples. :param torch.Tensor x: A single value or a batch of values batched along axis 0. :return: log probability densities as a one-dimensional :class:`~torch.Tensor` with same batch size as value and params. The shape of the result should be ``self.batch_size``. :rtype: torch.Tensor """ raise NotImplementedError
[docs] def score_parts(self, x, *args, **kwargs): """ Computes ingredients for stochastic gradient estimators of ELBO. The default implementation is correct both for non-reparameterized and for fully reparameterized distributions. Partially reparameterized distributions should override this method to compute correct `.score_function` and `.entropy_term` parts. :param torch.Tensor x: A single value or batch of values. :return: A `ScoreParts` object containing parts of the ELBO estimator. :rtype: ScoreParts """ log_prob = self.log_prob(x, *args, **kwargs) if self.has_rsample: return ScoreParts(log_prob=log_prob, score_function=0, entropy_term=log_prob) else: # XXX should the user be able to control inclusion of the entropy term? # See Roeder, Wu, Duvenaud (2017) "Sticking the Landing" return ScoreParts(log_prob=log_prob, score_function=log_prob, entropy_term=0)
[docs] def enumerate_support(self, expand=True): """ Returns a representation of the parametrized distribution's support, along the first dimension. This is implemented only by discrete distributions. Note that this returns support values of all the batched RVs in lock-step, rather than the full cartesian product. :param bool expand: whether to expand the result to a tensor of shape ``(n,) + batch_shape + event_shape``. If false, the return value has unexpanded shape ``(n,) + (1,)*len(batch_shape) + event_shape`` which can be broadcasted to the full shape. :return: An iterator over the distribution's discrete support. :rtype: iterator """ raise NotImplementedError("Support not implemented for {}".format(type(self)))