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134 lines
4.6 KiB
134 lines
4.6 KiB
import torch
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import torch.nn.functional as F
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from torch.distributions import constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.utils import (
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broadcast_all,
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lazy_property,
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logits_to_probs,
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probs_to_logits,
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)
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__all__ = ["NegativeBinomial"]
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class NegativeBinomial(Distribution):
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r"""
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Creates a Negative Binomial distribution, i.e. distribution
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of the number of successful independent and identical Bernoulli trials
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before :attr:`total_count` failures are achieved. The probability
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of success of each Bernoulli trial is :attr:`probs`.
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Args:
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total_count (float or Tensor): non-negative number of negative Bernoulli
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trials to stop, although the distribution is still valid for real
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valued count
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probs (Tensor): Event probabilities of success in the half open interval [0, 1)
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logits (Tensor): Event log-odds for probabilities of success
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"""
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arg_constraints = {
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"total_count": constraints.greater_than_eq(0),
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"probs": constraints.half_open_interval(0.0, 1.0),
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"logits": constraints.real,
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}
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support = constraints.nonnegative_integer
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def __init__(self, total_count, probs=None, logits=None, validate_args=None):
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if (probs is None) == (logits is None):
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raise ValueError(
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"Either `probs` or `logits` must be specified, but not both."
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)
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if probs is not None:
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(
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self.total_count,
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self.probs,
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) = broadcast_all(total_count, probs)
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self.total_count = self.total_count.type_as(self.probs)
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else:
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(
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self.total_count,
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self.logits,
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) = broadcast_all(total_count, logits)
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self.total_count = self.total_count.type_as(self.logits)
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self._param = self.probs if probs is not None else self.logits
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batch_shape = self._param.size()
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super().__init__(batch_shape, validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(NegativeBinomial, _instance)
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batch_shape = torch.Size(batch_shape)
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new.total_count = self.total_count.expand(batch_shape)
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if "probs" in self.__dict__:
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new.probs = self.probs.expand(batch_shape)
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new._param = new.probs
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if "logits" in self.__dict__:
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new.logits = self.logits.expand(batch_shape)
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new._param = new.logits
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super(NegativeBinomial, new).__init__(batch_shape, validate_args=False)
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new._validate_args = self._validate_args
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return new
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def _new(self, *args, **kwargs):
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return self._param.new(*args, **kwargs)
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@property
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def mean(self):
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return self.total_count * torch.exp(self.logits)
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@property
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def mode(self):
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return ((self.total_count - 1) * self.logits.exp()).floor().clamp(min=0.0)
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@property
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def variance(self):
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return self.mean / torch.sigmoid(-self.logits)
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@lazy_property
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def logits(self):
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return probs_to_logits(self.probs, is_binary=True)
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@lazy_property
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def probs(self):
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return logits_to_probs(self.logits, is_binary=True)
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@property
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def param_shape(self):
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return self._param.size()
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@lazy_property
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def _gamma(self):
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# Note we avoid validating because self.total_count can be zero.
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return torch.distributions.Gamma(
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concentration=self.total_count,
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rate=torch.exp(-self.logits),
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validate_args=False,
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)
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def sample(self, sample_shape=torch.Size()):
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with torch.no_grad():
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rate = self._gamma.sample(sample_shape=sample_shape)
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return torch.poisson(rate)
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def log_prob(self, value):
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if self._validate_args:
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self._validate_sample(value)
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log_unnormalized_prob = self.total_count * F.logsigmoid(
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-self.logits
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) + value * F.logsigmoid(self.logits)
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log_normalization = (
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-torch.lgamma(self.total_count + value)
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+ torch.lgamma(1.0 + value)
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+ torch.lgamma(self.total_count)
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)
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# The case self.total_count == 0 and value == 0 has probability 1 but
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# lgamma(0) is infinite. Handle this case separately using a function
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# that does not modify tensors in place to allow Jit compilation.
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log_normalization = log_normalization.masked_fill(
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self.total_count + value == 0.0, 0.0
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)
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return log_unnormalized_prob - log_normalization
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