scvi.external.mrvi_torch.TorchMRVAE

class scvi.external.mrvi_torch.TorchMRVAE(n_input, n_sample, n_batch, n_labels, n_latent=30, n_latent_u=10, encoder_n_hidden=128, encoder_n_layers=2, z_u_prior=True, z_u_prior_scale=0.0, u_prior_scale=0.0, u_prior_mixture=True, u_prior_mixture_k=20, learn_z_u_prior_scale=False, scale_observations=False, px_kwargs=None, qz_kwargs=None, qu_kwargs=None, n_obs_per_sample=None)

Bases: BaseModuleClass

Multi-resolution Variational Inference (MrVI) module.

Parameters:

• n_input (int) – Number of input features.

• n_sample (int) – Number of samples.

• n_batch (int) – Number of batches.

• n_labels (int) – Number of labels.

• n_latent (int (default: 30)) – Number of latent variables for z.

• n_latent_u (int (default: 10)) – Number of latent variables for u.

• encoder_n_hidden (int (default: 128)) – Number of hidden units in the encoder.

• encoder_n_layers (int (default: 2)) – Number of layers in the encoder.

• z_u_prior (bool (default: True)) – Whether to place a Gaussian prior on z given u.

• z_u_prior_scale (float (default: 0.0)) – Natural log of the scale parameter of the Gaussian prior placed on z given u. Only applies of learn_z_u_prior_scale is False.

• u_prior_scale (float (default: 0.0)) – Natural log of the scale parameter of the Gaussian prior placed on u. If u_prior_mixture is True, this scale applies to each mixture component distribution.

• u_prior_mixture (bool (default: True)) – Whether to use a mixture of Gaussians prior for u.

• u_prior_mixture_k (int (default: 20)) – Number of mixture components to use for the mixture of Gaussians prior on u.

• learn_z_u_prior_scale (bool (default: False)) – Whether to learn the scale parameter of the prior distribution of z given u.

• scale_observations (bool (default: False)) – Whether to scale the loss associated with each observation by the total number of observations linked to the associated sample.

• px_kwargs (dict | None (default: None)) – Keyword arguments for the generative model.

• qz_kwargs (dict | None (default: None)) – Keyword arguments for the inference model from u to z.

• qu_kwargs (dict | None (default: None)) – Keyword arguments for the inference model from x to u.

• training – Whether the model is in training mode.

• n_obs_per_sample (Tensor | None (default: None)) – Number of observations per sample.

Attributes table

training

Methods table

compute_h_from_x_eps(x, sample_index, ...[, ...])	Compute normalized gene expression from observations using predefined eps
generative(z, library, batch_index, label_index)	Generative model.
inference(x, sample_index[, mc_samples, ...])	Run the recognition model.
loss(tensors, inference_outputs, ...[, ...])	Compute the loss function value.

Attributes

TorchMRVAE.training: bool

Methods

TorchMRVAE.compute_h_from_x_eps(x, sample_index, batch_index, extra_eps, cf_sample=None, mc_samples=10)

Compute normalized gene expression from observations using predefined eps

TorchMRVAE.generative(z, library, batch_index, label_index, transform_batch=None)

Generative model.

Return type:

dict[str, Tensor | Distribution]

TorchMRVAE.inference(x, sample_index, mc_samples=None, cf_sample=None, use_mean=False)

Run the recognition model.

In the case of variational inference, this function will perform steps related to computing variational distribution parameters. In a VAE, this will involve running data through encoder networks.

This function should return a dictionary with str keys and Tensor values.

Return type:

dict[str, Tensor]

TorchMRVAE.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)

Compute the loss function value.

Return type:

LossOutput