scvi.model.base.BaseLatentModeModelClass#

class scvi.model.base.BaseLatentModeModelClass(adata=None)[source]#: Abstract base class for scvi-tools models that support latent mode.

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.
`latent_data_type`	The latent data type associated with this model.
`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#

`convert_legacy_save`(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
`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.
`load_registry`(dir_path[, prefix])	Return the full registry saved with the model.
`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.
`to_latent_mode`([mode])	Put the model into latent mode.
`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

BaseLatentModeModelClass.adata[source]#

Data attached to model instance.

Return type:: Union[AnnData, MuData]

adata_manager

BaseLatentModeModelClass.adata_manager[source]#

Manager instance associated with self.adata.

Return type:: AnnDataManager

device

BaseLatentModeModelClass.device[source]#

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

Return type:: str

history

BaseLatentModeModelClass.history[source]#: Returns computed metrics during training.

is_trained

BaseLatentModeModelClass.is_trained[source]#

Whether the model has been trained.

Return type:: bool

latent_data_type

BaseLatentModeModelClass.latent_data_type[source]#

The latent data type associated with this model.

Return type:: Optional[str]

test_indices

BaseLatentModeModelClass.test_indices[source]#

Observations that are in test set.

Return type:: ndarray

train_indices

BaseLatentModeModelClass.train_indices[source]#

Observations that are in train set.

Return type:: ndarray

validation_indices

BaseLatentModeModelClass.validation_indices[source]#

Observations that are in validation set.

Return type:: ndarray

Methods#

convert_legacy_save

classmethod BaseLatentModeModelClass.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#

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 at output_dir_path, error will be raised.
prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None

get_anndata_manager

BaseLatentModeModelClass.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 (Union[AnnData, MuData]) – 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:

Optional[AnnDataManager]

get_from_registry

BaseLatentModeModelClass.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 (Union[AnnData, MuData]) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

load

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

Instantiate a model from the saved output.

Parameters:

dir_path (str) – Path to saved outputs.
adata (Union[AnnData, MuData, None] (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 (Union[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 (Optional[str] (default: None)) – Prefix of saved file names.
backup_url (Optional[str] (default: None)) – 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_....

load_registry

static BaseLatentModeModelClass.load_registry(dir_path, prefix=None)[source]#

Return the full registry saved with the model.

Parameters:

dir_path (str) – Path to saved outputs.
prefix (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

register_manager

classmethod BaseLatentModeModelClass.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

BaseLatentModeModelClass.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 (Optional[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 BaseLatentModeModelClass.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

BaseLatentModeModelClass.to_device(device)[source]#

Move model to device.

Parameters:: device (Union[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

to_latent_mode

abstract BaseLatentModeModelClass.to_latent_mode(mode='dist', *args, **kwargs)[source]#

Put the model into latent mode.

The model is put into latent mode by registering new anndata fields required for latent mode support (can be model-specific) and marking the module as latent. Note that this modifies the anndata (and subsequently the model and module properties) in place. Please make a copy of those objects (before calling this function) if needed.

Parameters:: mode (str (default: 'dist')) – The latent data type used

train

abstract BaseLatentModeModelClass.train()[source]#: Trains the model.

view_anndata_setup

BaseLatentModeModelClass.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 (Union[AnnData, MuData, None] (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 BaseLatentModeModelClass.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 (Optional[str] (default: None)) – Prefix of saved file names.

Return type:

None