scvi.model.AmortizedLDA

scvi.model.AmortizedLDA#

class scvi.model.AmortizedLDA(adata, n_topics=20, n_hidden=128, cell_topic_prior=None, topic_feature_prior=None)[source]#

Amortized Latent Dirichlet Allocation [Blei et al., 2003].

Parameters:

adata (AnnData) – AnnData object that has been registered via setup_anndata().
n_topics (int (default: 20)) – Number of topics to model.
n_hidden (int (default: 128)) – Number of nodes in the hidden layer of the encoder.
cell_topic_prior (float | Sequence[float] | None (default: None)) – Prior of cell topic distribution. If None, defaults to 1 / n_topics.
topic_feature_prior (float | Sequence[float] | None (default: None)) – Prior of topic feature distribution. If None, defaults to 1 / n_topics.

Examples

>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.AmortizedLDA.setup_anndata(adata)
>>> model = scvi.model.AmortizedLDA(adata)
>>> model.train()
>>> feature_by_topic = model.get_feature_by_topic()
>>> adata.obsm["X_LDA"] = model.get_latent_representation()

Notes

See further usage examples in the following tutorial:

Topic Modeling with Amortized LDA

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.
`run_id`	Returns the run id of the model.
`run_name`	Returns the run name of the model.
`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.
`differential_abundance`(args, *kwargs)	Not implemented for this model class.
`get_anndata_manager`(adata[, required])	Retrieves the `AnnDataManager` for a given AnnData object.
`get_elbo`([adata, indices, batch_size])	Return the ELBO for the data.
`get_feature_by_topic`([n_samples])	Gets a Monte-Carlo estimate of the expectation of the feature by topic matrix.
`get_from_registry`(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
`get_latent_representation`([adata, indices, ...])	Converts a count matrix to an inferred topic distribution.
`get_normalized_expression`(args, *kwargs)	Not implemented for this model class.
`get_perplexity`([adata, indices, batch_size])	Computes approximate perplexity for adata.
`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_annbatch`(cls[, collection_path, ...])	Set up a model from on-disk AnnData files via the annbatch streaming loader.
`setup_anndata`(adata[, layer])	Sets up the `AnnData` object for this model.
`to_device`(device)	Move the model to the device.
`train`([max_epochs, accelerator, device, ...])	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#

AmortizedLDA.adata[source]#: Data attached to model instance.

AmortizedLDA.adata_manager[source]#: Manager instance associated with self.adata.

AmortizedLDA.device[source]#: The current device that the module’s params are on.

AmortizedLDA.get_normalized_function_name[source]#: What the get normalized functions name is

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

AmortizedLDA.is_trained[source]#: Whether the model has been trained.

AmortizedLDA.registry[source]#: Data attached to model instance.

AmortizedLDA.run_id[source]#: Returns the run id of the model. Used in MLFlow

AmortizedLDA.run_name[source]#: Returns the run name of the model. Used in MLFlow

AmortizedLDA.summary_string[source]#: Summary string of the model.

AmortizedLDA.test_indices[source]#: Observations that are in test set.

AmortizedLDA.train_indices[source]#: Observations that are in train set.

AmortizedLDA.validation_indices[source]#: Observations that are in validation set.

Methods#

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

Converts a legacy saved model (<v0.15.0) to the updated save format.

Parameters:

dir_path (str) – Path to the directory where the 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, an error will be raised.
prefix (str | None (default: None)) – Prefix of saved file names.
**save_kwargs – Keyword arguments passed into save().

Return type:

None

AmortizedLDA.data_registry(registry_key)[source]#

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

Parameters:: registry_key (str) – key of an object to get from self.data_registry
Return type:: ndarray | DataFrame
Returns:: The requested data.

AmortizedLDA.deregister_manager(adata=None)[source]#

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.

AmortizedLDA.differential_abundance(*args, **kwargs)[source]#

Not implemented for this model class.

Available in models that inherit from VAEMixin.

Raises:: NotImplementedError –

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

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 a manager instance for.
required (bool (default: False)) – If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type:

AnnDataManager | None

AmortizedLDA.get_elbo(adata=None, indices=None, batch_size=None)[source]#

Return the ELBO for the data.

The ELBO is a lower bound on the log likelihood of the data used for optimization of VAEs. Note, this is not the negative ELBO, higher is better.

Parameters:

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

Return type:

float

Returns:

The positive ELBO.

AmortizedLDA.get_feature_by_topic(n_samples=5000)[source]#

Gets a Monte-Carlo estimate of the expectation of the feature by topic matrix.

Parameters:

adata – AnnData to transform. If None, returns the feature by topic matrix for the source AnnData.
n_samples (default: 5000) – Number of samples to take for the Monte-Carlo estimate of the mean.

Return type:

DataFrame

Returns:

A n_var x n_topics Pandas DataFrame containing the feature by topic matrix.

AmortizedLDA.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 the data registry.
adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

AmortizedLDA.get_latent_representation(adata=None, indices=None, batch_size=None, n_samples=5000)[source]#

Converts a count matrix to an inferred topic distribution.

Parameters:

adata (AnnData | None (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 (default: None)) – Indices of cells in adata to use. If None, all cells are used.
batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
n_samples (int (default: 5000)) – Number of samples to take for the Monte-Carlo estimate of the mean.

Return type:

DataFrame

Returns:

A n_obs x n_topics Pandas DataFrame containing the normalized estimate of the topic distribution for each observation.

AmortizedLDA.get_normalized_expression(*args, **kwargs)[source]#

Not implemented for this model class.

Available in RNA models that inherit from RNASeqMixin.

Raises:: NotImplementedError –

AmortizedLDA.get_perplexity(adata=None, indices=None, batch_size=None)[source]#

Computes approximate perplexity for adata.

Perplexity is defined as exp(-1 * log-likelihood per count).

Parameters:

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

Return type:

float

Returns:

Perplexity.

AmortizedLDA.get_setup_arg(setup_arg)[source]#

Returns the string provided to setup of a specific setup_arg.

Return type:: attrdict

AmortizedLDA.get_state_registry(registry_key)[source]#

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

Return type:: attrdict

AmortizedLDA.get_var_names(legacy_mudata_format=False)[source]#

Variable names of input data.

Return type:: dict

classmethod AmortizedLDA.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None, datamodule=None, allowed_classes_names_list=None)[source]#

Instantiate a model from the saved output.

Parameters:

dir_path (str) – Path to saved outputs.
adata (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 (str | None (default: None)) – Prefix of saved file names.
backup_url (str | None (default: None)) – URL to retrieve saved outputs from if not present on disk.
datamodule (LightningDataModule | None (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.
allowed_classes_names_list (list[str] | None (default: None)) – list of allowed classes names to be loaded (besides the original class name)

Returns:

Model with loaded state dictionaries.

Examples

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

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

Return the full registry saved with the model.

Parameters:

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

Return type:

dict

Returns:

The full registry saved with the model

classmethod AmortizedLDA.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.

AmortizedLDA.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, datamodule=None, **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 | None (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, an error will be raised.
save_anndata (bool (default: False)) – If True, also saves the anndata
save_kwargs (dict | None (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 (LightningDataModule | None (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()

classmethod AmortizedLDA.setup_annbatch(cls, collection_path=None, paths=None, batch_key=None, labels_key=None, sample_key=None, unlabeled_category='Unknown', layer=None, categorical_covariate_keys=None, continuous_covariate_keys=None, rebuild=True, batch_size=4096, chunk_size=256, preload_nchunks=32, preload_to_gpu=True, dataset_size='20GB', shuffle=False, var_subset=None, merge=None, adatas=None, use_class_sampler=False, class_sampler_key=None, class_weights=None)[source]#

Set up a model from on-disk AnnData files via the annbatch streaming loader.

Builds (or reuses) a zarr-backed DatasetCollection from the supplied h5ad file paths, then wraps it in a AnnbatchDataModule ready for training.

Parameters:

collection_path (str | None (default: None)) – Directory where the zarr collection is written (or already exists). If None (default), a path is auto-generated as "./{ModelName}_annbatch.zarr" in the current working directory.
paths (list[str] | None (default: None)) – Paths to h5ad files that make up the training dataset. If None, an existing collection at collection_path is opened without rebuilding — useful when the zarr store was created by a previous call. Must be provided when the store does not exist yet.
batch_key (str | None (default: None)) – Column in obs to use as the batch variable.
labels_key (str | None (default: None)) – Column in obs to use as the cell-type / label variable.
sample_key (str | None (default: None)) – Column in obs to use as the sample variable. Used by models like MrVI.
unlabeled_category (str (default: 'Unknown')) – Value used to mark unlabeled cells in labels_key. Required by semi-supervised models such as SCANVI. Defaults to "Unknown".
layer (str | None (default: None)) – Layer in the h5ad files to use as the count matrix. None uses adata.X (falling back to adata.raw.X when a raw slot exists).
categorical_covariate_keys (list[str] | None (default: None)) – Additional categorical covariate columns in obs.
continuous_covariate_keys (list[str] | None (default: None)) – Additional continuous covariate columns in obs.
rebuild (bool (default: True)) – If True, always rebuild the zarr collection even if it already exists on disk. If False (default), reuse an existing collection and skip the (potentially expensive) add_adatas step.
batch_size (int (default: 4096)) – Number of cells per batch yielded by the Loader.
chunk_size (int (default: 256)) – Number of cells loaded from disk contiguously per read.
preload_nchunks (int (default: 32)) – Number of chunks to preload and shuffle in memory.
preload_to_gpu (bool (default: True)) – Whether the loader should move data to GPU before yielding.
dataset_size (int | str (default: '20GB')) – Number of observations to load into memory for shuffling / pre-processing when building the collection, or annbatch’s human-readable size strings, e.g. "20GB".
shuffle (bool (default: False)) – Whether to pre-shuffle cells when building the collection.
var_subset (list[str] | None (default: None)) – Optional list of gene names to restrict the collection to (passed as var_subset to add_adatas).
merge (Optional[Literal['same', 'unique', 'first', 'only']] (default: None)) – How annbatch should merge var metadata across inputs. Passed through to annbatch.DatasetCollection.add_adatas().

Returns:

AnnbatchDataModule A configured datamodule whose registry can be passed directly to the model constructor, e.g. model = SCVI(registry=dm.registry).

Notes

After training, saving and reloading the model requires reconstructing the datamodule manually and passing it to load() via the datamodule argument — the same pattern used by all custom datamodules.

classmethod AmortizedLDA.setup_anndata(adata, layer=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:

adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
layer (str | None (default: None)) – if not None, uses this as the key in adata.layers for raw count data.

Return type:

AnnData | None

AmortizedLDA.to_device(device)[source]#

Move the model to the device.

Parameters:: device (str | int | device) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (e.g., 0), ‘cuda:X’ where X is the GPU index (e.g. ‘cuda:0’), or a torch.device object (including XLA devices for TPU). 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

AmortizedLDA.train(max_epochs=None, accelerator='auto', device='auto', train_size=None, validation_size=None, shuffle_set_split=True, batch_size=128, early_stopping=False, lr=None, training_plan=None, datasplitter_kwargs=None, plan_config=None, plan_kwargs=None, trainer_config=None, **trainer_kwargs)[source]#

Train the model.

Parameters:

max_epochs (int | None (default: None)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])
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.
train_size (float | None (default: None)) – Size of training set in the range [0.0, 1.0].
validation_size (float | None (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.
shuffle_set_split (bool (default: True)) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.
batch_size (int (default: 128)) – Minibatch size to use during training. If None, no minibatching occurs and all data is copied to device (e.g., GPU).
early_stopping (bool (default: False)) – Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.
lr (float | None (default: None)) – Optimiser learning rate (default optimiser is ClippedAdam). Specifying optimiser via plan_kwargs overrides this choice of lr.
training_plan (PyroTrainingPlan | None (default: None)) – Training plan PyroTrainingPlan.
datasplitter_kwargs (dict | None (default: None)) – Additional keyword arguments passed into DataSplitter.
plan_kwargs (KwargsLike | None (default: None)) – Keyword args for PyroTrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.
plan_config (KwargsLike | None (default: None)) – Configuration object or mapping used to build PyroTrainingPlan. Values in plan_kwargs and explicit arguments take precedence.
trainer_config (KwargsLike | None (default: None)) – Configuration object or mapping used to build Trainer. Values in trainer_kwargs and explicit arguments take precedence.
**trainer_kwargs – Other keyword args for Trainer.

AmortizedLDA.transfer_fields(adata, **kwargs)[source]#

Transfer fields from a model to an AnnData object.

Return type:: AnnData

AmortizedLDA.update_setup_method_args(setup_method_args)[source]#

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.

AmortizedLDA.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 | 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

AmortizedLDA.view_registry(hide_state_registries=False)[source]#

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

static AmortizedLDA.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 | None (default: None)) – Prefix of saved file names.

Return type:

None

AmortizedLDA.view_setup_method_args()[source]#

Prints setup kwargs used to produce a given registry.

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

scvi.model.AmortizedLDA

Contents

scvi.model.AmortizedLDA#

Attributes table#

Methods table#

Attributes#

Methods#