scvi.model.base.BaseModelClass#

class scvi.model.base.BaseModelClass(adata=None)[source]#

Abstract class for scvi-tools models.

Attributes table#

adata

Data attached to model instance.

adata_manager

Manager instance associated with self.adata.

device

The current device that the module's params are on.

history

Returns computed metrics during training.

is_trained

Whether the model has been trained.

test_indices

Observations that are in test set.

train_indices

Observations that are in train set.

validation_indices

Observations that are in validation set.

Methods table#

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

get_from_registry(adata, registry_key)

Returns the object in AnnData associated with the key in the data registry.

load(dir_path[, adata, use_gpu, prefix])

Instantiate a model from the saved output.

register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

save(dir_path[, prefix, overwrite, save_anndata])

Save the state of the model.

setup_anndata(adata, *args, **kwargs)

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train()

Trains the model.

view_anndata_setup([adata, ...])

Print summary of the setup for the initial AnnData or a given AnnData object.

view_setup_args(dir_path[, prefix])

Print args used to setup a saved model.

Attributes#

adata#

BaseModelClass.adata#

Data attached to model instance.

Return type

AnnData

adata_manager#

BaseModelClass.adata_manager#

Manager instance associated with self.adata.

Return type

AnnDataManager

device#

BaseModelClass.device#

The current device that the module’s params are on.

Return type

str

history#

BaseModelClass.history#

Returns computed metrics during training.

is_trained#

BaseModelClass.is_trained#

Whether the model has been trained.

Return type

bool

test_indices#

BaseModelClass.test_indices#

Observations that are in test set.

Return type

ndarray

train_indices#

BaseModelClass.train_indices#

Observations that are in train set.

Return type

ndarray

validation_indices#

BaseModelClass.validation_indices#

Observations that are in validation set.

Return type

ndarray

Methods#

get_anndata_manager#

BaseModelClass.get_anndata_manager(adata, required=False)[source]#

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

Requires self.id has been set. Checks for an AnnDataManager specific to this model instance.

Parameters
adata : AnnData

AnnData object to find manager instance for.

required : bool (default: False)

If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type

AnnDataManager | NoneOptional[AnnDataManager]

get_from_registry#

BaseModelClass.get_from_registry(adata, registry_key)[source]#

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.

Parameters
registry_key : str

key of object to get from data registry.

adata : AnnData

AnnData to pull data from.

Return type

ndarray

Returns

The requested data as a NumPy array.

load#

classmethod BaseModelClass.load(dir_path, adata=None, use_gpu=None, prefix=None)[source]#

Instantiate a model from the saved output.

Parameters
dir_path : str

Path to saved outputs.

adata : AnnData | NoneOptional[AnnData] (default: None)

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 : str | int | bool | NoneUnion[str, int, bool, None] (default: None)

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 : str | NoneOptional[str] (default: None)

Prefix of saved file names.

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 BaseModelClass.register_manager(adata_manager)[source]#

Registers an AnnDataManager instance with this model class.

Stores the AnnDataManager reference in a class-specific manager store. Intended for use in the setup_anndata() class method followed up by retrieval of the AnnDataManager 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 previous AnnDataManager.

save#

BaseModelClass.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[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 : str

Path to a directory.

prefix : str | NoneOptional[str] (default: None)

Prefix to prepend to saved file names.

overwrite : bool (default: False)

Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.

save_anndata : bool (default: False)

If True, also saves the anndata

anndata_write_kwargs

Kwargs for write()

setup_anndata#

abstract classmethod BaseModelClass.setup_anndata(adata, *args, **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.

Each model class deriving from this class provides parameters to this method according to its needs. To operate correctly with the model initialization, the implementation must call register_manager() on a model-specific instance of AnnDataManager.

to_device#

BaseModelClass.to_device(device)[source]#

Move model to device.

Parameters
device : str | intUnion[str, int]

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#

abstract BaseModelClass.train()[source]#

Trains the model.

view_anndata_setup#

BaseModelClass.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

Print summary of the setup for the initial AnnData or a given AnnData object.

Parameters
adata : AnnData | NoneOptional[AnnData] (default: None)

AnnData object setup with setup_anndata or transfer_fields().

hide_state_registries : bool (default: False)

If True, prints a shortened summary without details of each state registry.

Return type

None

view_setup_args#

static BaseModelClass.view_setup_args(dir_path, prefix=None)[source]#

Print args used to setup a saved model.

Parameters
dir_path : str

Path to saved outputs.

prefix : str | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type

None