scvi.external.VELOVI

scvi.external.VELOVI#

class scvi.external.VELOVI(adata, n_hidden=256, n_latent=10, n_layers=1, dropout_rate=0.1, gamma_init_data=False, linear_decoder=False, **model_kwargs)[source]#

BETA Velocity Variational Inference [Gayoso et al., 2023].

Parameters:

  • adata (AnnData) – AnnData object that has been registered via setup_anndata().

  • n_hidden (int (default: 256)) – 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.

  • dropout_rate (float (default: 0.1)) – Dropout rate for neural networks.

  • gamma_init_data (bool (default: False)) – Initialize gamma using the data-driven technique.

  • linear_decoder (bool (default: False)) – Use a linear decoder from latent space to time.

  • **model_kwargs – Keyword args for VELOVAE

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.

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.

deregister_manager([adata])

Deregisters the AnnDataManager instance associated with adata.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object.

get_directional_uncertainty([adata, ...])

get_elbo([adata, indices, batch_size, ...])

Compute the evidence lower bound (ELBO) on the data.

get_expression_fit([adata, indices, ...])

Returns the fitted spliced and unspliced abundance (s(t) and u(t)).

get_from_registry(adata, registry_key)

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

get_gene_likelihood([adata, indices, ...])

Returns the likelihood per gene.

get_latent_representation([adata, indices, ...])

Compute the latent representation of the data.

get_latent_time([adata, indices, gene_list, ...])

Returns the cells by genes latent time.

get_marginal_ll([adata, indices, ...])

Compute the marginal log-likehood of the data.

get_permutation_scores(labels_key[, adata])

Compute permutation scores.

get_rates()

get_reconstruction_error([adata, indices, ...])

Compute the reconstruction error on the data.

get_state_assignment([adata, indices, ...])

Returns cells by genes by states probabilities.

get_velocity([adata, indices, gene_list, ...])

Returns cells by genes velocity estimates.

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, spliced_layer, ...)

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train([max_epochs, lr, weight_decay, ...])

Train 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#

VELOVI.adata[source]#

Data attached to model instance.

VELOVI.adata_manager[source]#

Manager instance associated with self.adata.

VELOVI.device[source]#

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

VELOVI.history[source]#

Returns computed metrics during training.

VELOVI.is_trained[source]#

Whether the model has been trained.

VELOVI.summary_string[source]#

Summary string of the model.

VELOVI.test_indices[source]#

Observations that are in test set.

VELOVI.train_indices[source]#

Observations that are in train set.

VELOVI.validation_indices[source]#

Observations that are in validation set.

Methods#

classmethod VELOVI.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 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 (str | None (default: None)) – Prefix of saved file names.

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

Return type:

None

VELOVI.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.

VELOVI.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 manager instance for.

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

Return type:

AnnDataManager | None

VELOVI.get_directional_uncertainty(adata=None, n_samples=50, gene_list=None, n_jobs=-1)[source]#
VELOVI.get_elbo(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, **kwargs)[source]#

Compute the evidence lower bound (ELBO) on the data.

The ELBO is the reconstruction error plus the Kullback-Leibler (KL) divergences between the variational distributions and the priors. It is different from the marginal log-likelihood; specifically, it is a lower bound on the marginal log-likelihood plus a term that is constant with respect to the variational distribution. It still gives good insights on the modeling of the data and is fast to compute.

Parameters:

  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • return_mean (bool (default: True)) – Whether to return the mean of the ELBO or the ELBO for each observation.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

float

Returns:

Evidence lower bound (ELBO) of the data.

Notes

This is not the negative ELBO, so higher is better.

VELOVI.get_expression_fit(adata=None, indices=None, gene_list=None, n_samples=1, batch_size=None, return_mean=True, return_numpy=None, restrict_to_latent_dim=None)[source]#

Returns the fitted spliced and unspliced abundance (s(t) and u(t)).

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.

  • gene_list (Sequence[str] | None (default: None)) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • n_samples (int (default: 1)) – Number of posterior samples to use for estimation.

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to batch_size.

  • return_mean (bool (default: True)) – Whether to return the mean of the samples.

  • return_numpy (bool | None (default: None)) – Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type:

ndarray | DataFrame

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

VELOVI.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 | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

VELOVI.get_gene_likelihood(adata=None, indices=None, gene_list=None, n_samples=1, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns the likelihood per gene. Higher is better.

This is denoted as \(\rho_n\) in the scVI paper.

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.

  • transform_batch –

    Batch to condition on. One of the following:

    • None: real observed batch is used.

    • int: batch transform_batch is used.

  • gene_list (Sequence[str] | None (default: None)) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • library_size – Scale the expression frequencies to a common library size. This allows gene expression levels to be interpreted on a common scale of relevant magnitude. If set to "latent", use the latent libary size.

  • n_samples (int (default: 1)) – Number of posterior samples to use for estimation.

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to batch_size.

  • return_mean (bool (default: True)) – Whether to return the mean of the samples.

  • return_numpy (bool | None (default: None)) – Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type:

ndarray | DataFrame

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

VELOVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None, return_dist=False, dataloader=None)[source]#

Compute the latent representation of the data.

This is typically denoted as \(z_n\).

Parameters:

  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • give_mean (bool (default: True)) – If True, returns the mean of the latent distribution. If False, returns an estimate of the mean using mc_samples Monte Carlo samples.

  • mc_samples (int (default: 5000)) – Number of Monte Carlo samples to use for the estimator for distributions with no closed-form mean (e.g., the logistic normal distribution). Not used if give_mean is True or if return_dist is True.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • return_dist (bool (default: False)) – If True, returns the mean and variance of the latent distribution. Otherwise, returns the mean of the latent distribution.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] | tuple[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

Returns:

An array of shape (n_obs, n_latent) if return_dist is False. Otherwise, returns a tuple of arrays (n_obs, n_latent) with the mean and variance of the latent distribution.

VELOVI.get_latent_time(adata=None, indices=None, gene_list=None, time_statistic='mean', n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns the cells by genes latent time.

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.

  • gene_list (Sequence[str] | None (default: None)) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • time_statistic (Literal['mean', 'max'] (default: 'mean')) – Whether to compute expected time over states, or maximum a posteriori time over maximal probability state.

  • n_samples (int (default: 1)) – Number of posterior samples to use for estimation.

  • n_samples_overall (int | None (default: None)) – Number of overall samples to return. Setting this forces n_samples=1.

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to batch_size.

  • return_mean (bool (default: True)) – Whether to return the mean of the samples.

  • return_numpy (bool | None (default: None)) – Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type:

ndarray | DataFrame

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

VELOVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None, return_mean=True, dataloader=None, **kwargs)[source]#

Compute the marginal log-likehood of the data.

The computation here is a biased estimator of the marginal log-likelihood of the data.

Parameters:

  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None.

  • n_mc_samples (int (default: 1000)) – Number of Monte Carlo samples to use for the estimator. Passed into the module’s marginal_ll method.

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None.

  • return_mean (bool (default: True)) – Whether to return the mean of the marginal log-likelihood or the marginal-log likelihood for each observation.

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • **kwargs – Additional keyword arguments to pass into the module’s marginal_ll method.

Return type:

float | Tensor

Returns:

If True, returns the mean marginal log-likelihood. Otherwise returns a tensor of shape (n_obs,) with the marginal log-likelihood for each observation.

Notes

This is not the negative log-likelihood, so higher is better.

VELOVI.get_permutation_scores(labels_key, adata=None)[source]#

Compute permutation scores.

Parameters:

  • labels_key (str) – Key in adata.obs encoding cell types.

  • 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.

Return type:

tuple[DataFrame, AnnData]

Returns:

Tuple of DataFrame and AnnData. DataFrame is genes by cell types with score per cell type. AnnData is the permutated version of the original AnnData.

VELOVI.get_rates()[source]#
VELOVI.get_reconstruction_error(adata=None, indices=None, batch_size=None, dataloader=None, return_mean=True, **kwargs)[source]#

Compute the reconstruction error on the data.

The reconstruction error is the negative log likelihood of the data given the latent variables. It is different from the marginal log-likelihood, but still gives good insights on the modeling of the data and is fast to compute. This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample.

Parameters:

  • adata (AnnData | None (default: None)) – AnnData object with var_names in the same order as the ones used to train the model. If None and dataloader is also None, it defaults to the object used to initialize the model.

  • indices (Sequence[int] | None (default: None)) – Indices of observations in adata to use. If None, defaults to all observations. Ignored if dataloader is not None

  • batch_size (int | None (default: None)) – Minibatch size for the forward pass. If None, defaults to scvi.settings.batch_size. Ignored if dataloader is not None

  • dataloader (Iterator[dict[str, Tensor | None]] (default: None)) – An iterator over minibatches of data on which to compute the metric. The minibatches should be formatted as a dictionary of Tensor with keys as expected by the model. If None, a dataloader is created from adata.

  • return_mean (bool (default: True)) – Whether to return the mean reconstruction loss or the reconstruction loss for each observation.

  • **kwargs – Additional keyword arguments to pass into the forward method of the module.

Return type:

dict[str, float]

Returns:

Reconstruction error for the data.

Notes

This is not the negative reconstruction error, so higher is better.

VELOVI.get_state_assignment(adata=None, indices=None, gene_list=None, hard_assignment=False, n_samples=20, batch_size=None, return_mean=True, return_numpy=None)[source]#

Returns cells by genes by states probabilities.

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.

  • gene_list (Sequence[int] | None (default: None)) – Return frequencies of expression for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • hard_assignment (bool (default: False)) – Return a hard state assignment

  • n_samples (int (default: 20)) – Number of posterior samples to use for estimation.

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to batch_size.

  • return_mean (bool (default: True)) – Whether to return the mean of the samples.

  • return_numpy (bool | None (default: None)) – Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

Return type:

tuple[ndarray | DataFrame | list[str]]

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

VELOVI.get_velocity(adata=None, indices=None, gene_list=None, n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None, velo_statistic='mean', velo_mode='spliced', clip=True)[source]#

Returns cells by genes velocity estimates.

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.

  • gene_list (Sequence[str] | None (default: None)) – Return velocities for a subset of genes. This can save memory when working with large datasets and few genes are of interest.

  • n_samples (int (default: 1)) – Number of posterior samples to use for estimation for each cell.

  • n_samples_overall (int | None (default: None)) – Number of overall samples to return. Setting this forces n_samples=1.

  • batch_size (int | None (default: None)) – Minibatch size for data loading into model. Defaults to batch_size.

  • return_mean (bool (default: True)) – Whether to return the mean of the samples.

  • return_numpy (bool | None (default: None)) – Return a ndarray instead of a DataFrame. DataFrame includes gene names as columns. If either n_samples=1 or return_mean=True, defaults to False. Otherwise, it defaults to True.

  • velo_statistic (str (default: 'mean')) – Whether to compute expected velocity over states, or maximum a posteriori velocity over maximal probability state.

  • velo_mode (Literal['spliced', 'unspliced'] (default: 'spliced')) – Compute ds/dt or du/dt.

  • clip (bool (default: True)) – Clip to minus spliced value

Return type:

ndarray | DataFrame

Returns:

If n_samples > 1 and return_mean is False, then the shape is (samples, cells, genes). Otherwise, shape is (cells, genes). In this case, return type is DataFrame unless return_numpy is True.

classmethod VELOVI.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=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.

  • 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.

Returns:

Model with loaded state dictionaries.

Examples

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

VELOVI.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=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 | 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, 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.

  • anndata_write_kwargs – Kwargs for write()

classmethod VELOVI.setup_anndata(adata, spliced_layer, unspliced_layer, **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.

  • spliced_layer (str) – Layer in adata with spliced normalized expression.

  • unspliced_layer (str) – Layer in adata with unspliced normalized expression.

Return type:

AnnData | None

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

VELOVI.to_device(device)[source]#

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
VELOVI.train(max_epochs=500, lr=0.01, weight_decay=0.01, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, batch_size=256, early_stopping=True, gradient_clip_val=10, plan_kwargs=None, external_indexing=None, **trainer_kwargs)[source]#

Train the model.

Parameters:

  • max_epochs (int | None (default: 500)) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

  • lr (float (default: 0.01)) – Learning rate for optimization.

  • weight_decay (float (default: 0.01)) – Weight decay for optimization.

  • accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

  • devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.

  • train_size (float (default: 0.9)) – 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.

  • batch_size (int (default: 256)) – Minibatch size to use during training.

  • early_stopping (bool (default: True)) – Perform early stopping. Additional arguments can be passed in **kwargs. See Trainer for further options.

  • gradient_clip_val (float (default: 10)) – Value for gradient clipping.

  • plan_kwargs (dict | None (default: None)) – Keyword args for TrainingPlan. Keyword arguments passed to this method will overwrite values present in plan_kwargs, when appropriate.

  • external_indexing (list[ndarray] (default: None)) – A list of data split indices in the order of training, validation, and test sets. Validation and test set are not required and can be left empty.

  • **trainer_kwargs – Other keyword args for Trainer.

VELOVI.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

static VELOVI.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

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