# scvi.module.AmortizedLDAPyroModule#

class scvi.module.AmortizedLDAPyroModule(n_input, n_topics, n_hidden, cell_topic_prior=None, topic_feature_prior=None)[source]#

An amortized implementation of Latent Dirichlet Allocation implemented in Pyro.

This module uses auto encoding variational Bayes to optimize the latent variables in the model. In particular, a fully-connected neural network is used as an encoder, which takes in feature counts as input and outputs the parameters of cell topic distribution. To employ the reparametrization trick stably, the Dirichlet priors are approximated by a Logistic-Normal distribution. The input feature counts tensor is a cell by features Bag-of-Words(BoW) representation of the counts. I.e. the model treats each cell’s feature vector as ordered, not as unordered as in a Multinomial distribution.

Parameters:

## Methods table#

 get_elbo(x, library, n_obs) Computes ELBO. get_topic_distribution(x, n_samples) Converts x to its inferred topic distribution. topic_by_feature(n_samples) Gets a Monte-Carlo estimate of the expectation of the topic by feature matrix.

## Attributes#

guide

AmortizedLDAPyroModule.guide[source]#

model

AmortizedLDAPyroModule.model[source]#

training

AmortizedLDAPyroModule.training: bool#

## Methods#

get_elbo

AmortizedLDAPyroModule.get_elbo(x, library, n_obs)[source]#

Computes ELBO.

Parameters:
Return type:

float

Returns:

The positive ELBO.

get_topic_distribution

AmortizedLDAPyroModule.get_topic_distribution(x, n_samples)[source]#

Converts x to its inferred topic distribution.

Parameters:
Return type:

Tensor

Returns:

A x.shape[0] x n_topics tensor containing the normalized topic distribution.

topic_by_feature

AmortizedLDAPyroModule.topic_by_feature(n_samples)[source]#

Gets a Monte-Carlo estimate of the expectation of the topic by feature matrix.

Assumes the module has already been trained.

Parameters:

n_samples (int) – Number of samples to take for the Monte-Carlo estimate of the mean.

Return type:

Tensor

Returns:

A n_topics x n_input tensor containing the topic by feature matrix.