spatialvi.module.JVAE

class spatialvi.module.JVAE(dim_input_list, total_genes, indices_mappings, gene_likelihoods, model_library_bools, library_log_means, library_log_vars, n_latent=10, n_layers_encoder_individual=1, n_layers_encoder_shared=1, dim_hidden_encoder=64, n_layers_decoder_individual=0, n_layers_decoder_shared=0, dim_hidden_decoder_individual=64, dim_hidden_decoder_shared=64, dropout_rate_encoder=0.2, dropout_rate_decoder=0.2, n_batch=0, n_labels=0, dispersion='gene-batch', log_variational=True)

Joint variational auto-encoder for imputing missing genes in spatial data.

Implementation of gimVI [].

Parameters:

• dim_input_list (list[int]) – List of number of input genes for each dataset.

• total_genes (int) – Total number of different genes.

• indices_mappings (list[ndarray | slice]) – List of mappings from model inputs to model output locations.

• gene_likelihoods (list[str]) – List of distributions: ‘zinb’, ‘nb’, or ‘poisson’.

• model_library_bools (list[bool]) – Whether to model library size with a latent variable per dataset.

• library_log_means (list[ndarray | None]) – List of 1 x n_batch arrays of log library size means.

• library_log_vars (list[ndarray | None]) – List of 1 x n_batch arrays of log library size variances.

• n_latent (int) – Dimension of latent space.

• n_layers_encoder_individual (int) – Number of individual encoder layers.

• n_layers_encoder_shared (int) – Number of shared encoder layers.

• dim_hidden_encoder (int) – Hidden layer dimension for encoder.

• n_layers_decoder_individual (int) – Number of individual decoder layers.

• n_layers_decoder_shared (int) – Number of shared decoder layers.

• dim_hidden_decoder_individual (int) – Hidden layer dimension for individual decoder.

• dim_hidden_decoder_shared (int) – Hidden layer dimension for shared decoder.

• dropout_rate_encoder (float) – Dropout rate for encoder.

• dropout_rate_decoder (float) – Dropout rate for decoder.

• n_batch (int) – Total number of batches.

• n_labels (int) – Total number of labels.

• dispersion (str) – Dispersion parameterization: ‘gene’, ‘gene-batch’, ‘gene-label’, or ‘gene-cell’.

• log_variational (bool) – Log(data+1) prior to encoding for numerical stability.

__init__(dim_input_list, total_genes, indices_mappings, gene_likelihoods, model_library_bools, library_log_means, library_log_vars, n_latent=10, n_layers_encoder_individual=1, n_layers_encoder_shared=1, dim_hidden_encoder=64, n_layers_decoder_individual=0, n_layers_decoder_shared=0, dim_hidden_decoder_individual=64, dim_hidden_decoder_shared=64, dropout_rate_encoder=0.2, dropout_rate_decoder=0.2, n_batch=0, n_labels=0, dispersion='gene-batch', log_variational=True)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(dim_input_list, total_genes, ...[, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(tensors[, ...])	Forward pass through the network.
generative(z, library[, batch_index, y, ...])	Run the generative model.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_sample_rate(x, batch_index, *_, **__)	Get the sample rate for the model.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
inference(x[, mode, n_samples, batch_index])	Run the inference model.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
loss(tensors, inference_outputs, ...[, ...])	Return the reconstruction loss and the Kullback divergences.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
on_load(model, **kwargs)	Callback function run in load().
parameters([recurse])	Return an iterator over module parameters.
reconstruction_loss(x, px_rate, px_r, ...)	Compute the reconstruction loss.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
sample(*args, **kwargs)	Generate samples from the learned model.
sample_from_posterior_l(x[, mode, deterministic])	Sample the tensor of library sizes from the posterior.
sample_from_posterior_z(x[, mode, deterministic])	Sample tensor of latent values from the posterior.
sample_rate(x, mode, batch_index[, y, ...])	Return the tensor of scaled frequencies of expression.
sample_scale(x, mode, batch_index[, y, ...])	Return the tensor of predicted frequencies of expression.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
device
dump_patches
training