simple_scvi.MyPyroModel

simple_scvi.MyPyroModel#

class simple_scvi.MyPyroModel(adata, n_hidden=128, n_latent=10, n_layers=1, **model_kwargs)#

Skeleton for a pyro version of a scvi-tools model.

Please use this skeleton to create new models.

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.
n_layers (int (default: 1)) – Number of hidden layers used for encoder and decoder NNs.
**model_kwargs – Keyword args for MyModule

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> mypackage.MyPyroModel.setup_anndata(adata, batch_key="batch")
>>> vae = mypackage.MyModel(adata)
>>> vae.train()
>>> adata.obsm["X_mymodel"] = vae.get_latent_representation()

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.
`get_normalized_function_name`	What the get normalized functions name is
`history`	Returns computed metrics during training.
`is_trained`	Whether the model has been trained.
`registry`	Data attached to model instance.
`summary_string`	Summary string of the 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.
`data_registry`(registry_key)	Returns the object in AnnData associated with the key in the data registry.
`deregister_manager`([adata])	Deregisters the `AnnDataManager` instance associated with `adata`.
`get_anndata_manager`(adata[, required])	Retrieves the `AnnDataManager` for a given AnnData object.
`get_from_registry`(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
`get_latent`([adata, indices, batch_size])	Return the latent representation for each cell.
`get_normalized_expression`(args, *kwargs)
`get_setup_arg`(setup_arg)	Returns the string provided to setup of a specific setup_arg.
`get_state_registry`(registry_key)	Returns the state registry for the AnnDataField registered with this instance.
`get_var_names`([legacy_mudata_format])	Variable names of input data.
`load`(dir_path[, adata, accelerator, device, ...])	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 the state of the model.
`setup_anndata`(adata[, batch_key, ...])	Sets up the `AnnData` object for this model.
`to_device`(device)	Move model to device.
`train`([max_epochs, use_gpu, train_size, ...])	Train the model.
`transfer_fields`(adata, **kwargs)	Transfer fields from a model to an AnnData object.
`update_setup_method_args`(setup_method_args)	Update setup method args.
`view_anndata_setup`([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
`view_registry`([hide_state_registries])	Prints summary of the registry.
`view_setup_args`(dir_path[, prefix])	Print args used to setup a saved model.
`view_setup_method_args`()	Prints setup kwargs used to produce a given registry.

Attributes#

adata#

MyPyroModel.adata#: Data attached to model instance.

adata_manager#

MyPyroModel.adata_manager#: Manager instance associated with self.adata.

device#

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

get_normalized_function_name#

MyPyroModel.get_normalized_function_name#: What the get normalized functions name is

history#

MyPyroModel.history#: Returns computed metrics during training.

is_trained#

MyPyroModel.is_trained#: Whether the model has been trained.

registry#

MyPyroModel.registry#: Data attached to model instance.

summary_string#

MyPyroModel.summary_string#: Summary string of the model.

test_indices#

MyPyroModel.test_indices#: Observations that are in test set.

train_indices#

MyPyroModel.train_indices#: Observations that are in train set.

validation_indices#

MyPyroModel.validation_indices#: Observations that are in validation set.

Methods#

convert_legacy_save#

classmethod MyPyroModel.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)#

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.
**save_kwargs – Keyword arguments passed into save().

Return type:

None

data_registry#

MyPyroModel.data_registry(registry_key)#

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

Parameters:

registry_key (str) – key of object to get from self.data_registry

Return type:

ndarray | DataFrame

Returns:

: The requested data.

deregister_manager#

MyPyroModel.deregister_manager(adata=None)#

Deregisters the AnnDataManager instance associated with adata.

If adata is None, deregisters all AnnDataManager instances in both the class and instance-specific manager stores, except for the one associated with this model instance.

get_anndata_manager#

MyPyroModel.get_anndata_manager(adata, required=False)#

Retrieves the AnnDataManager for a given AnnData object.

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

Parameters:

adata (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:

AnnDataManager | None

get_from_registry#

MyPyroModel.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.

Parameters:

registry_key (str) – key of object to get from data registry.
adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

: The requested data as a NumPy array.

get_latent#

MyPyroModel.get_latent(adata=None, indices=None, batch_size=None)#

Return the latent representation for each cell.

This is denoted as \(z_n\) in our manuscripts.

Parameters:

adata (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 (Optional[Sequence[int]] (default: None)) – Indices of cells in adata to use. If None, all cells are used.
batch_size (Optional[int] (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Returns:

: -latent_representation (ndarray)

Low-dimensional representation for each cell

get_normalized_expression#

MyPyroModel.get_normalized_expression(*args, **kwargs)#

get_setup_arg#

MyPyroModel.get_setup_arg(setup_arg)#

Returns the string provided to setup of a specific setup_arg.

Return type:: attrdict

get_state_registry#

MyPyroModel.get_state_registry(registry_key)#

Returns the state registry for the AnnDataField registered with this instance.

Return type:: attrdict

get_var_names#

MyPyroModel.get_var_names(legacy_mudata_format=False)#

Variable names of input data.

Return type:: dict

load#

classmethod MyPyroModel.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None, datamodule=None)#

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. If False, will load the model without AnnData.
accelerator (str (default: 'auto')) – Supports passing different accelerator types ("cpu", "gpu", "tpu", "ipu", "hpu", "mps, "auto") as well as custom accelerator instances.
device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or "auto" for automatic selection based on the chosen accelerator. If set to "auto" and accelerator is not determined to be "cpu", then device will be set to the first available device.
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.
datamodule (Optional[LightningDataModule] (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.

Returns:

: Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....

load_registry#

static MyPyroModel.load_registry(dir_path, prefix=None)#

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 MyPyroModel.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 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#

MyPyroModel.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, datamodule=None, **anndata_write_kwargs)#

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
save_kwargs (Optional[dict] (default: None)) – Keyword arguments passed into save().
legacy_mudata_format (bool (default: False)) – If True, saves the model var_names in the legacy format if the model was trained with a MuData object. The legacy format is a flat array with variable names across all modalities concatenated, while the new format is a dictionary with keys corresponding to the modality names and values corresponding to the variable names for each modality.
datamodule (Optional[LightningDataModule] (default: None)) – EXPERIMENTAL A LightningDataModule instance to use for training in place of the default DataSplitter. Can only be passed in if the model was not initialized with AnnData.
anndata_write_kwargs – Kwargs for write()

setup_anndata#

classmethod MyPyroModel.setup_anndata(adata, batch_key=None, labels_key=None, layer=None, categorical_covariate_keys=None, continuous_covariate_keys=None, **kwargs)#

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:

adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
batch_key (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.
labels_key (Optional[str] (default: None)) – key in adata.obs for label information. Categories will automatically be converted into integer categories and saved to adata.obs['_scvi_labels']. If None, assigns the same label to all the data.
layer (Optional[str] (default: None)) – if not None, uses this as the key in adata.layers for raw count data.
categorical_covariate_keys (Optional[List[str]] (default: None)) – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
continuous_covariate_keys (Optional[List[str]] (default: None)) – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.

Return type:

Optional[AnnData]

Returns:

: None. Adds the following fields:

.uns[‘_scvi’]: scvi setup dictionary
.obs[‘_scvi_labels’]: labels encoded as integers
.obs[‘_scvi_batch’]: batch encoded as integers

to_device#

MyPyroModel.to_device(device)#

Move model to device.

Parameters:: device (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#

MyPyroModel.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, plan_kwargs=None, **trainer_kwargs)#

Train the model.

Parameters:

max_epochs (Optional[int] (default: None)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])
use_gpu (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), or use CPU (if False).
train_size (float (default: 0.9)) – Size of training set in the range [0.0, 1.0].
validation_size (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.
plan_kwargs (Optional[dict] (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
**trainer_kwargs – Other keyword args for Trainer.

transfer_fields#

MyPyroModel.transfer_fields(adata, **kwargs)#

Transfer fields from a model to an AnnData object.

Return type:: AnnData

update_setup_method_args#

MyPyroModel.update_setup_method_args(setup_method_args)#

Update setup method args.

Parameters:: setup_method_args (dict) – This is a bit of a misnomer, this is a dict representing kwargs of the setup method that will be used to update the existing values in the registry of this instance.

view_anndata_setup#

MyPyroModel.view_anndata_setup(adata=None, hide_state_registries=False)#

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_registry#

MyPyroModel.view_registry(hide_state_registries=False)#

Prints summary of the registry.

Parameters:: 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 MyPyroModel.view_setup_args(dir_path, prefix=None)#

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

view_setup_method_args#

MyPyroModel.view_setup_method_args()#

Prints setup kwargs used to produce a given registry.

Parameters:: registry – Registry produced by an AnnDataManager.
Return type:: None

simple_scvi.MyPyroModel

Contents

simple_scvi.MyPyroModel#

Attributes table#

Methods table#

Attributes#

adata#

adata_manager#

device#

get_normalized_function_name#

history#

is_trained#

registry#

summary_string#

test_indices#

train_indices#

validation_indices#

Methods#

convert_legacy_save#

data_registry#

deregister_manager#

get_anndata_manager#

get_from_registry#

get_latent#

get_normalized_expression#

get_setup_arg#

get_state_registry#

get_var_names#

load#

load_registry#

register_manager#

save#

setup_anndata#

to_device#

train#

transfer_fields#

update_setup_method_args#

view_anndata_setup#

view_registry#

view_setup_args#

view_setup_method_args#