networkvi.nn.GOLayers

class networkvi.nn.GOLayers(geneobj, genemodel_out_blocks, ensembl_ids, obo_file: str, map_ensembl_go: list | ~numpy.ndarray, standard_go_size: int = 6, input_dropout: float = 0.1, n_layers: int = 5, n_hidden: int = 128, activation_fn: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ReLU'>, dynamic_go_size: bool = False, register_rancon: bool = False, remove_rancon: bool = False, n_cat_list=None, inject_covariates: bool = True, first_layer_inject_covariates: bool = True, last_layer_inject_covariates: bool = True, *args, **kwargs)

A helper class to build GOLayers for a neural network.

Parameters:

• geneobj – geneobj generated using GeneLayers.

• genemodel_out_blocks – out blocks sparse matrix of object generated using GeneLayers.

• ensembl_ids – ENSEMBL-IDs of features.

• obo_file – Path .obo file of GO.

• map_ensembl_go – List of .gaf files with mappings of Ensembl IDs to GO.

• standard_go_size – Standard size of GO nodes in GO Layers.

• input_dropout – Dropout rate to apply to each of the hidden layers

• n_layers – The number of fully-connected hidden layers

• n_hidden – The number of nodes per hidden layer

• activation_fn – Which activation function to use

• n_cat_list – A list containing, for each category of interest, the number of categories. Each category will be included using a one-hot encoding.

• inject_covariates – Whether to deeply inject covariates into all layers of the endecoder. If False, covariates will only be included in the input layer.

• first_layer_inject_covariates – Whether to deeply inject covariates into all layers of the decoder. If False, covariates will only be included in the input layer.

• last_layer_inject_covariates – Whether to inject covariates into all layers of the decoder. If False, covariates will only be included in the input layer.

__init__(geneobj, genemodel_out_blocks, ensembl_ids, obo_file: str, map_ensembl_go: list | ~numpy.ndarray, standard_go_size: int = 6, input_dropout: float = 0.1, n_layers: int = 5, n_hidden: int = 128, activation_fn: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ReLU'>, dynamic_go_size: bool = False, register_rancon: bool = False, remove_rancon: bool = False, n_cat_list=None, inject_covariates: bool = True, first_layer_inject_covariates: bool = True, last_layer_inject_covariates: bool = True, *args, **kwargs)

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

Methods

__init__(geneobj, genemodel_out_blocks, ...)	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()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x, *cat_list[, cont_input])	Define the computation performed at every call.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_ds_terms_and_parents(unknown, goobj, gos)
get_extra_state()	Return any extra state to include in the module's state_dict.
get_goobj_info()
get_n_parameters()
get_neurons_available(dynamic_go_cfg, ...)	Returns the OGM's number of neurons available for distribution per height level.
get_ordered_gos(ensemblids, genetic_json, ...)
get_overflow_info(dynamic_go_cfg, ...)
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_parents(term)
get_sizes_by_level([name])	Returns the OGM's layersizes ordered per depth/ogmdepth/height as dict mapping level -> list of layersizes
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
get_total_size()	Returns the OGM's total number of neurons.
half()	Casts all floating point parameters and buffers to half datatype.
initialize_go_obj(mode, obo_file, max_level, ...)
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.
modules()	Return an iterator over all modules in the network.
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.
parameters([recurse])	Return an iterator over module parameters.
patch_bup_ontology(goobj, relations)	Patch ontology built from the bottom up (i.e. with filtering for max_level height).
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_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_pre_hook(hook)	Register a pre-hook for the load_state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_ensemblids(unknown, goobj, gos, ensemblids)	Sets the ensemblid attribute for every GOTerm of the GO object.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_layersize(goobj, relations, ...)	Sets the layersize attribute for every GOTerm of the GO object.
set_layersize_dynamic(dynamic_go_cfg, goobj, ...)
set_topnode_and_unknown(goobj, ontology, ...)	Sets and returns the top (root) node and UNKNOWN node of the ontology.
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
dump_patches
training