# scvi.distributions.ZeroInflatedNegativeBinomial#

class scvi.distributions.ZeroInflatedNegativeBinomial(total_count=None, probs=None, logits=None, mu=None, theta=None, zi_logits=None, scale=None, validate_args=False)[source]#

Zero-inflated negative binomial distribution.

One of the following parameterizations must be provided:

(1), (total_count, probs) where total_count is the number of failures until the experiment is stopped and probs the success probability. (2), (mu, theta) parameterization, which is the one used by scvi-tools. These parameters respectively control the mean and inverse dispersion of the distribution.

In the (mu, theta) parameterization, samples from the negative binomial are generated as follows:

1. $$w \sim \textrm{Gamma}(\underbrace{\theta}_{\text{shape}}, \underbrace{\theta/\mu}_{\text{rate}})$$

2. $$x \sim \textrm{Poisson}(w)$$

Parameters:

## 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. mode Returns the mode of the distribution. stddev Returns the standard deviation of the distribution. support variance Returns the variance of the distribution. zi_logits ZI logits. zi_probs

## Methods table#

 cdf(value) Returns the cumulative density/mass function evaluated at value. 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) Log probability. 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]) Sample from the distribution. Generates n samples or n batches of samples if the distribution parameters are batched. Sets whether validation is enabled or disabled.

## Attributes#

arg_constraints

ZeroInflatedNegativeBinomial.arg_constraints = {'mu': GreaterThanEq(lower_bound=0), 'theta': GreaterThanEq(lower_bound=0), 'zi_logits': Real(), 'zi_probs': HalfOpenInterval(lower_bound=0.0, upper_bound=1.0)}#

batch_shape

ZeroInflatedNegativeBinomial.batch_shape[source]#

Returns the shape over which parameters are batched.

event_shape

ZeroInflatedNegativeBinomial.event_shape[source]#

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

has_enumerate_support

ZeroInflatedNegativeBinomial.has_enumerate_support = False#

has_rsample

ZeroInflatedNegativeBinomial.has_rsample = False#

mean

ZeroInflatedNegativeBinomial.mean[source]#

mode

ZeroInflatedNegativeBinomial.mode[source]#

Returns the mode of the distribution.

stddev

ZeroInflatedNegativeBinomial.stddev[source]#

Returns the standard deviation of the distribution.

support

ZeroInflatedNegativeBinomial.support = IntegerGreaterThan(lower_bound=0)#

variance

ZeroInflatedNegativeBinomial.variance[source]#

zi_logits

ZeroInflatedNegativeBinomial.zi_logits[source]#

ZI logits.

zi_probs

ZeroInflatedNegativeBinomial.zi_probs[source]#

## Methods#

cdf

ZeroInflatedNegativeBinomial.cdf(value)[source]#

Returns the cumulative density/mass function evaluated at value.

Parameters:

value (Tensor) –

Return type:

Tensor

entropy

ZeroInflatedNegativeBinomial.entropy()[source]#

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

Return type:

Tensor

enumerate_support

ZeroInflatedNegativeBinomial.enumerate_support(expand=True)[source]#

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, [, , ...

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.

Return type:

Tensor

expand

ZeroInflatedNegativeBinomial.expand(batch_shape, _instance=None)[source]#

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

ZeroInflatedNegativeBinomial.icdf(value)[source]#

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

Parameters:

value (Tensor) –

Return type:

Tensor

log_prob

ZeroInflatedNegativeBinomial.log_prob(value)[source]#

Log probability.

Parameters:

value (Tensor) –

Return type:

Tensor

perplexity

ZeroInflatedNegativeBinomial.perplexity()[source]#

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

Return type:

Tensor

rsample

ZeroInflatedNegativeBinomial.rsample(sample_shape=torch.Size([]))[source]#

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

Parameters:

sample_shape (Size) –

Return type:

Tensor

sample

ZeroInflatedNegativeBinomial.sample(sample_shape=None)[source]#

Sample from the distribution.

Parameters:

sample_shape (Optional[Union[Size, Tuple]]) –

Return type:

Tensor

sample_n

ZeroInflatedNegativeBinomial.sample_n(n)[source]#

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

Parameters:

n (int) –

Return type:

Tensor

set_default_validate_args

static ZeroInflatedNegativeBinomial.set_default_validate_args(value)[source]#

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.

Return type:

None