scvi.model.JaxSCVI#
- class scvi.model.JaxSCVI(adata, n_hidden=128, n_latent=10, dropout_rate=0.1, gene_likelihood='nb', **model_kwargs)[source]#
EXPERIMENTAL single-cell Variational Inference [Lopez18], but with a Jax backend.
This implementation is in a very experimental state. API is completely subject to change.
- Parameters
- adata :
AnnData
AnnData object that has been registered via
setup_anndata()
.- n_hidden :
int
(default:128
) Number of nodes per hidden layer.
- n_latent :
int
(default:10
) Dimensionality of the latent space.
- dropout_rate :
float
(default:0.1
) Dropout rate for neural networks.
- gene_likelihood : {‘nb’, ‘poisson’}
Literal
[‘nb’, ‘poisson’] (default:'nb'
) One of:
'nb'
- Negative binomial distribution'poisson'
- Poisson distribution
- **model_kwargs
Keyword args for
JaxVAE
- adata :
Examples
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.JaxSCVI.setup_anndata(adata, batch_key="batch") >>> vae = scvi.model.SCVI(adata) >>> vae.train() >>> adata.obsm["X_scVI"] = vae.get_latent_representation()
Attributes table#
Data attached to model instance. |
|
Manager instance associated with self.adata. |
|
The current device that the module's params are on. |
|
Returns computed metrics during training. |
|
Whether the model has been trained. |
|
Observations that are in test set. |
|
Observations that are in train set. |
|
Observations that are in validation set. |
Methods table#
|
Converts a legacy saved model (<v0.15.0) to the updated save format. |
|
Retrieves the |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Return the latent representation for each cell. |
|
Instantiate a model from the saved output. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
Move model to device. |
|
|
Train the model. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
|
Print args used to setup a saved model. |
Attributes#
adata#
adata_manager#
- JaxSCVI.adata_manager#
Manager instance associated with self.adata.
- Return type
device#
- JaxSCVI.device#
history#
- JaxSCVI.history#
Returns computed metrics during training.
is_trained#
module#
- JaxSCVI.module#
test_indices#
train_indices#
validation_indices#
Methods#
convert_legacy_save#
- classmethod JaxSCVI.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)#
Converts a legacy saved model (<v0.15.0) to the updated save format.
- Parameters
- dir_path :
str
Path to directory where legacy model is saved.
- output_dir_path :
str
Path to save converted save files.
- overwrite :
bool
(default:False
) Overwrite existing data or not. If
False
and directory already exists atoutput_dir_path
, error will be raised.- prefix :
str
|None
Optional
[str
] (default:None
) Prefix of saved file names.
- dir_path :
- Return type
get_anndata_manager#
- JaxSCVI.get_anndata_manager(adata, required=False)#
Retrieves the
AnnDataManager
for a given AnnData object specific to this model instance.Requires
self.id
has been set. Checks for anAnnDataManager
specific to this model instance.- Parameters
- Return type
get_from_registry#
- JaxSCVI.get_from_registry(adata, registry_key)#
Returns the object in AnnData associated with the key in the data registry.
AnnData object should be registered with the model prior to calling this function via the
self._validate_anndata
method.
get_latent_representation#
- JaxSCVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=1, batch_size=None)[source]#
Return the latent representation for each cell.
This is denoted as \(z_n\) in our manuscripts.
- Parameters
- adata :
AnnData
|None
Optional
[AnnData
] (default:None
) AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
- indices :
Sequence
[int
] |None
Optional
[Sequence
[int
]] (default:None
) Indices of cells in adata to use. If None, all cells are used.
- batch_size :
int
|None
Optional
[int
] (default:None
) Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
- adata :
- Return type
- Returns
-latent_representation (
ndarray
) Low-dimensional representation for each cell
load#
- JaxSCVI.load()[source]#
Instantiate a model from the saved output.
- Parameters
- dir_path
Path to saved outputs.
- adata
AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model.
- use_gpu
Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).
- prefix
Prefix of saved file names.
- backup_url
URL to retrieve saved outputs from if not present on disk.
- Returns
Model with loaded state dictionaries.
Examples
>>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_....
register_manager#
- classmethod JaxSCVI.register_manager(adata_manager)#
Registers an
AnnDataManager
instance with this model class.Stores the
AnnDataManager
reference in a class-specific manager store. Intended for use in thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
via the_get_most_recent_anndata_manager()
method in the model init method.Notes
Subsequent calls to this method with an
AnnDataManager
instance referring to the same underlying AnnData object will overwrite the reference to previousAnnDataManager
.
save#
- JaxSCVI.save()[source]#
Save the state of the model.
Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.
- Parameters
- dir_path
Path to a directory.
- prefix
Prefix to prepend to saved file names.
- overwrite
Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.
- save_anndata
If True, also saves the anndata
- anndata_write_kwargs
Kwargs for
write()
setup_anndata#
- classmethod JaxSCVI.setup_anndata(adata, layer=None, batch_key=None, **kwargs)[source]#
- Sets up the
AnnData
object for this model. A mapping will be created between data fields used by this model to their respective locations in adata.
None of the data in adata are modified. Only adds fields to adata.
- Parameters
- layer :
str
|None
Optional
[str
] (default:None
) if not None, uses this as the key in adata.layers for raw count data.
- batch_key :
str
|None
Optional
[str
] (default:None
) key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.
- layer :
- Sets up the
to_device#
- JaxSCVI.to_device()[source]#
Move model to device.
- Parameters
- device
Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.
Examples
>>> adata = scvi.data.synthetic_iid() >>> model = scvi.model.SCVI(adata) >>> model.to_device('cpu') # moves model to CPU >>> model.to_device('cuda:0') # moves model to GPU 0 >>> model.to_device(0) # also moves model to GPU 0
train#
- JaxSCVI.train(max_epochs=None, check_val_every_n_epoch=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, lr=0.001)[source]#
Train the model.
- Parameters
- max_epochs :
int
|None
Optional
[int
] (default:None
) Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])
- use_gpu :
str
|int
|bool
|None
Union
[str
,int
,bool
,None
] (default:None
) Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g., ‘cuda:0’), or use CPU (if False).
- train_size :
float
(default:0.9
) Size of training set in the range [0.0, 1.0].
- validation_size :
float
|None
Optional
[float
] (default:None
) Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.
- batch_size :
int
(default:128
) Minibatch size to use during training.
- max_epochs :
view_anndata_setup#
- JaxSCVI.view_anndata_setup(adata=None, hide_state_registries=False)#
Print summary of the setup for the initial AnnData or a given AnnData object.