scvi.distributions.ZeroInflatedLogNormal#
- class scvi.distributions.ZeroInflatedLogNormal(mu, scale, zi_logits, normal_mu=None, validate_args=False)[source]#
Zero-inflated log-normal distribution.
A mixture distribution of a point mass at zero and a log-normal distribution. This is a mixed distribution with discrete support at zero and continuous support over (0, ∞).
In the (mu, scale, zi_logits) parameterization, samples are generated as follows:
\(\pi = \textrm{sigmoid}(\texttt{zi\_logits})\)
\(b \sim \textrm{Bernoulli}(\pi)\)
If \(b = 1\): \(x = 0\)
If \(b = 0\): \(z \sim \textrm{Normal}(\mu, \sigma)\), \(x = \exp(z)\)
The probability mass/density function (mixed discrete-continuous) is:
\[\begin{split}p(x; \mu, \sigma, \pi) = \begin{cases} \pi & \text{if } x = 0 \\ (1 - \pi) \cdot f_{LN}(x) & \text{if } x > 0 \end{cases}\end{split}\]where \(f_{LN}(x; \mu, \sigma) = \frac{1}{x \sigma \sqrt{2\pi}} \exp\left(-\frac{(\ln x - \mu)^2}{2\sigma^2}\right)\) is the log-normal density.
Note: The point mass at \(x = 0\) has probability \(\pi\), and the continuous log-normal component (for \(x > 0\)) has probability \((1-\pi)\).
- Parameters:
mu (
Tensor) – Mean of the normal distribution in log space.scale (
Tensor) – Standard deviation of the normal distribution in log space.zi_logits (
Tensor) – Logits scale of zero inflation probability.normal_mu (
Tensor|None(default:None)) – Normalized mean expression of the distribution.validate_args (
bool(default:False)) – Raise ValueError if arguments do not match constraints.
Attributes table#
Returns the shape over which parameters are batched. |
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Returns the shape of a single sample (without batching). |
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Mean of the zero-inflated distribution. |
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Returns the mode of the distribution. |
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Returns the standard deviation of the distribution. |
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Variance of the zero-inflated distribution. |
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ZI logits. |
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ZI probabilities. |
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Methods table#
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Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution. |
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Returns entropy of distribution, batched over batch_shape. |
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Returns tensor containing all values supported by a discrete distribution. |
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Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. |
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Get normalized values. |
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Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution. |
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Log probability. |
Returns perplexity of distribution, batched over batch_shape. |
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Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. |
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Sample from the distribution. |
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Generates n samples or n batches of samples if the distribution parameters are batched. |
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Sets whether validation is enabled or disabled. |
Attributes#
-
ZeroInflatedLogNormal.arg_constraints:
dict[str,Constraint] = {'loc': Optional(Real()), 'scale': Optional(GreaterThan(lower_bound=0)), 'zi_logits': Optional(Real())}#
- ZeroInflatedLogNormal.clear_cache#
- ZeroInflatedLogNormal.has_enumerate_support = False#
- ZeroInflatedLogNormal.has_rsample = True#
- ZeroInflatedLogNormal.support = GreaterThanEq(lower_bound=0.0)#
Methods#
- ZeroInflatedLogNormal.cdf(value)[source]#
Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution.
- ZeroInflatedLogNormal.entropy()[source]#
Returns entropy of distribution, batched over batch_shape.
- Returns:
Tensor of shape batch_shape.
- ZeroInflatedLogNormal.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, [[0], [1], ...
To iterate over the full Cartesian product use itertools.product(m.enumerate_support()).
- ZeroInflatedLogNormal.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
expandon 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 (default:
None) – new instance provided by subclasses that need to override .expand.
- Returns:
New distribution instance with batch dimensions expanded to batch_size.
- ZeroInflatedLogNormal.icdf(value)[source]#
Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution.
- ZeroInflatedLogNormal.perplexity()[source]#
Returns perplexity of distribution, batched over batch_shape.
- Return type:
- Returns:
Tensor of shape batch_shape.
- ZeroInflatedLogNormal.rsample(sample_shape=())[source]#
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. Samples first from base distribution and applies transform() for every transform in the list.
- Return type:
- ZeroInflatedLogNormal.sample_n(n)[source]#
Generates n samples or n batches of samples if the distribution parameters are batched.
- Return type:
- static ZeroInflatedLogNormal.set_default_validate_args(value)[source]#
Sets whether validation is enabled or disabled.
The default behavior mimics Python’s
assertstatement: validation is on by default, but is disabled if Python is run in optimized mode (viapython -O). Validation may be expensive, so you may want to disable it once a model is working.