scvi.distributions.NegativeBinomialMixture#

class scvi.distributions.NegativeBinomialMixture(mu1, mu2, theta1, mixture_logits, theta2=None, validate_args=False)[source]#

Negative binomial mixture distribution.

See NegativeBinomial for further description of parameters.

Parameters:
mu1 : Tensor

Mean of the component 1 distribution.

mu2 : Tensor

Mean of the component 2 distribution.

theta1 : Tensor

Inverse dispersion for component 1.

mixture_logits : Tensor

Logits scale probability of belonging to component 1.

theta2 : Tensor | NoneOptional[Tensor] (default: None)

Inverse dispersion for component 1. If None, assumed to be equal to theta1.

validate_args : bool (default: False)

Raise ValueError if arguments do not match constraints

Attributes table#

arg_constraints

batch_shape

Returns the shape over which parameters are batched.

event_shape

Returns the shape of a single sample (without batching).

has_enumerate_support

has_rsample

mean

Returns the mean of the distribution.

mixture_probs

rtype:

Tensor

stddev

Returns the standard deviation of the distribution.

support

variance

Returns the variance of the distribution.

Methods table#

cdf(value)

Returns the cumulative density/mass function evaluated at value.

entropy()

Returns entropy of distribution, batched over batch_shape.

enumerate_support([expand])

Returns tensor containing all values supported by a discrete distribution.

expand(batch_shape[, _instance])

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape.

icdf(value)

Returns the inverse cumulative density/mass function evaluated at value.

log_prob(value)

Returns the log of the probability density/mass function evaluated at value.

perplexity()

Returns perplexity of distribution, batched over batch_shape.

rsample([sample_shape])

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample([sample_shape])

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

sample_n(n)

Generates n samples or n batches of samples if the distribution parameters are batched.

set_default_validate_args(value)

Sets whether validation is enabled or disabled.

Attributes#

arg_constraints#

NegativeBinomialMixture.arg_constraints = {'mixture_logits': Real(), 'mixture_probs': HalfOpenInterval(lower_bound=0.0, upper_bound=1.0), 'mu1': GreaterThanEq(lower_bound=0), 'mu2': GreaterThanEq(lower_bound=0), 'theta1': GreaterThanEq(lower_bound=0)}#

batch_shape#

NegativeBinomialMixture.batch_shape#

Returns the shape over which parameters are batched.

event_shape#

NegativeBinomialMixture.event_shape#

Returns the shape of a single sample (without batching).

has_enumerate_support#

NegativeBinomialMixture.has_enumerate_support = False#

has_rsample#

NegativeBinomialMixture.has_rsample = False#

mean#

NegativeBinomialMixture.mean#

mixture_probs#

NegativeBinomialMixture.mixture_probs#
Return type:

Tensor

stddev#

NegativeBinomialMixture.stddev#

Returns the standard deviation of the distribution.

support#

NegativeBinomialMixture.support = IntegerGreaterThan(lower_bound=0)#

variance#

NegativeBinomialMixture.variance#

Returns the variance of the distribution.

Methods#

cdf#

NegativeBinomialMixture.cdf(value)#

Returns the cumulative density/mass function evaluated at value.

Parameters:
value : Tensor

entropy#

NegativeBinomialMixture.entropy()#

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

enumerate_support#

NegativeBinomialMixture.enumerate_support(expand=True)#

Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step [[0, 0], [1, 1], …]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use itertools.product(m.enumerate_support()).

Parameters:
expand : bool

whether to expand the support over the batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

expand#

NegativeBinomialMixture.expand(batch_shape, _instance=None)#

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created.

Parameters:
batch_shape : torch.Size

the desired expanded size.

_instance

new instance provided by subclasses that need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to batch_size.

icdf#

NegativeBinomialMixture.icdf(value)#

Returns the inverse cumulative density/mass function evaluated at value.

Parameters:
value : Tensor

log_prob#

NegativeBinomialMixture.log_prob(value)[source]#

Returns the log of the probability density/mass function evaluated at value.

Parameters:
value : Tensor

Return type:

Tensor

perplexity#

NegativeBinomialMixture.perplexity()#

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

rsample#

NegativeBinomialMixture.rsample(sample_shape=torch.Size([]))#

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample#

NegativeBinomialMixture.sample(sample_shape=torch.Size([]))[source]#

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

Return type:

Tensor

sample_n#

NegativeBinomialMixture.sample_n(n)#

Generates n samples or n batches of samples if the distribution parameters are batched.

set_default_validate_args#

static NegativeBinomialMixture.set_default_validate_args(value)#

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Parameters:
value : bool

Whether to enable validation.