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.