scvi.module.PEAKVAE#
- class scvi.module.PEAKVAE(n_input_regions, n_batch=0, n_hidden=None, n_latent=None, n_layers_encoder=2, n_layers_decoder=2, n_continuous_cov=0, n_cats_per_cov=None, dropout_rate=0.1, model_depth=True, region_factors=True, use_batch_norm='none', use_layer_norm='both', latent_distribution='normal', deeply_inject_covariates=False, encode_covariates=False)[source]#
Bases:
BaseModuleClass
Variational auto-encoder model for ATAC-seq data.
This is an implementation of the peakVI model descibed in.
- Parameters:
- n_input_regions :
int
Number of input regions.
- n_batch :
int
(default:0
) Number of batches, if 0, no batch correction is performed.
- n_hidden :
int
|None
Optional
[int
] (default:None
) Number of nodes per hidden layer. If None, defaults to square root of number of regions.
- n_latent :
int
|None
Optional
[int
] (default:None
) Dimensionality of the latent space. If None, defaults to square root of n_hidden.
- n_layers_encoder :
int
(default:2
) Number of hidden layers used for encoder NN.
- n_layers_decoder :
int
(default:2
) Number of hidden layers used for decoder NN.
- dropout_rate :
float
(default:0.1
) Dropout rate for neural networks
- model_depth :
bool
(default:True
) Model library size factors or not.
- region_factors :
bool
(default:True
) Include region-specific factors in the model
- use_batch_norm : {‘encoder’, ‘decoder’, ‘none’, ‘both’}
Literal
[‘encoder’, ‘decoder’, ‘none’, ‘both’] (default:'none'
) One of the following
'encoder'
- use batch normalization in the encoder only'decoder'
- use batch normalization in the decoder only'none'
- do not use batch normalization (default)'both'
- use batch normalization in both the encoder and decoder
- use_layer_norm : {‘encoder’, ‘decoder’, ‘none’, ‘both’}
Literal
[‘encoder’, ‘decoder’, ‘none’, ‘both’] (default:'both'
) One of the following
'encoder'
- use layer normalization in the encoder only'decoder'
- use layer normalization in the decoder only'none'
- do not use layer normalization'both'
- use layer normalization in both the encoder and decoder (default)
- latent_distribution :
str
(default:'normal'
) which latent distribution to use, options are
'normal'
- Normal distribution (default)'ln'
- Logistic normal distribution (Normal(0, I) transformed by softmax)
- deeply_inject_covariates :
bool
(default:False
) Whether to deeply inject covariates into all layers of the decoder. If False (default), covairates will only be included in the input layer.
- n_input_regions :
Attributes table#
Methods table#
|
Runs the generative model. |
|
|
|
Helper function used in forward pass. |
|
Compute the loss for a minibatch of data. |
Attributes#
T_destination#
alias of TypeVar(‘T_destination’, bound=Mapping
[str
, Tensor
])
.. autoattribute:: PEAKVAE.T_destination
device
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- PEAKVAE.device#
dump_patches#
- PEAKVAE.dump_patches: bool = False#
This allows better BC support for
load_state_dict()
. Instate_dict()
, the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See_load_from_state_dict
on how to use this information in loading.If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.
training#
Methods#
generative#
get_reconstruction_loss#
inference#
loss#
- PEAKVAE.loss(tensors, inference_outputs, generative_outputs, kl_weight=1.0)[source]#
Compute the loss for a minibatch of data.
This function uses the outputs of the inference and generative functions to compute a loss. This many optionally include other penalty terms, which should be computed here.
This function should return an object of type
LossRecorder
.