scvi.model.SCVI#
- class scvi.model.SCVI(adata, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', latent_distribution='normal', **model_kwargs)[source]#
single-cell Variational Inference [Lopez et al., 2018].
- Parameters:
adata (
AnnData
) – AnnData object that has been registered viasetup_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.dropout_rate (
float
(default:0.1
)) – Dropout rate for neural networks.dispersion (
Literal
[‘gene’, ‘gene-batch’, ‘gene-label’, ‘gene-cell’] (default:'gene'
)) –One of the following:
'gene'
- dispersion parameter of NB is constant per gene across cells'gene-batch'
- dispersion can differ between different batches'gene-label'
- dispersion can differ between different labels'gene-cell'
- dispersion can differ for every gene in every cell
gene_likelihood (
Literal
[‘zinb’, ‘nb’, ‘poisson’] (default:'zinb'
)) –One of:
'nb'
- Negative binomial distribution'zinb'
- Zero-inflated negative binomial distribution'poisson'
- Poisson distribution
latent_distribution (
Literal
[‘normal’, ‘ln’] (default:'normal'
)) –One of:
'normal'
- Normal distribution'ln'
- Logistic normal distribution (Normal(0, I) transformed by softmax)
**model_kwargs – Keyword args for
VAE
Examples
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.SCVI.setup_anndata(adata, batch_key="batch") >>> vae = scvi.model.SCVI(adata) >>> vae.train() >>> adata.obsm["X_scVI"] = vae.get_latent_representation() >>> adata.obsm["X_normalized_scVI"] = vae.get_normalized_expression()
Notes
See further usage examples in the following tutorials:
Attributes table#
Data attached to model instance. |
|
Manager instance associated with self.adata. |
|
The current device that the module's params are on. |
|
Returns computed metrics during training. |
|
Whether the model has been trained. |
|
The latent data type associated with this model. |
|
Observations that are in test set. |
|
Observations that are in train set. |
|
Observations that are in validation set. |
Methods table#
|
Converts a legacy saved model (<v0.15.0) to the updated save format. |
|
|
|
Retrieves the |
|
Return the ELBO for the data. |
|
Generate gene-gene correlation matrix using scvi uncertainty and expression. |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Returns the latent library size for each cell. |
|
Return the latent representation for each cell. |
|
Estimates for the parameters of the likelihood \(p(x \mid z)\) |
|
Return the marginal LL for the data. |
|
Returns the normalized (decoded) gene expression. |
|
Return the reconstruction error for the data. |
|
Instantiate a model from the saved output. |
|
Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021]. |
|
Return the full registry saved with the model. |
|
Generate observation samples from the posterior predictive distribution. |
|
Prepare data for query integration. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
Put the model into latent mode. |
|
Train the model. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
|
Print args used to setup a saved model. |
Attributes#
adata
adata_manager
device
history
is_trained
latent_data_type
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod SCVI.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. IfFalse
and directory already exists atoutput_dir_path
, error will be raised.prefix (
Optional
[str
] (default:None
)) – Prefix of saved file names.
- Return type:
differential_expression
- SCVI.differential_expression(adata=None, groupby=None, group1=None, group2=None, idx1=None, idx2=None, mode='change', delta=0.25, batch_size=None, all_stats=True, batch_correction=False, batchid1=None, batchid2=None, fdr_target=0.05, silent=False, **kwargs)[source]#
-
A unified method for differential expression analysis.
Implements
'vanilla'
DE [Lopez et al., 2018] and'change'
mode DE [Boyeau et al., 2019].- 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.groupby (
Optional
[str
] (default:None
)) – The key of the observations grouping to consider.group1 (
Optional
[Iterable
[str
]] (default:None
)) – Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which comparison shall be restricted, or all groups in groupby (default).group2 (
Optional
[str
] (default:None
)) – If None, compare each group in group1 to the union of the rest of the groups in groupby. If a group identifier, compare with respect to this group.idx1 (
Union
[Sequence
[int
],Sequence
[bool
],str
,None
] (default:None
)) – idx1 and idx2 can be used as an alternative to the AnnData keys. Custom identifier for group1 that can be of three sorts: (1) a boolean mask, (2) indices, or (3) a string. If it is a string, then it will query indices that verifies conditions on adata.obs, as described inpandas.DataFrame.query()
If idx1 is not None, this option overrides group1 and group2.idx2 (
Union
[Sequence
[int
],Sequence
[bool
],str
,None
] (default:None
)) – Custom identifier for group2 that has the same properties as idx1. By default, includes all cells not specified in idx1.mode (
Literal
[‘vanilla’, ‘change’] (default:'change'
)) – Method for differential expression. See user guide for full explanation.delta (
float
(default:0.25
)) – specific case of region inducing differential expression. In this case, we suppose that \(R \setminus [-\delta, \delta]\) does not induce differential expression (change model default case).batch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.all_stats (
bool
(default:True
)) – Concatenate count statistics (e.g., mean expression group 1) to DE results.batch_correction (
bool
(default:False
)) – Whether to correct for batch effects in DE inference.batchid1 (
Optional
[Iterable
[str
]] (default:None
)) – Subset of categories from batch_key registered insetup_anndata
, e.g. [‘batch1’, ‘batch2’, ‘batch3’], for group1. Only used if batch_correction is True, and by default all categories are used.batchid2 (
Optional
[Iterable
[str
]] (default:None
)) – Same as batchid1 for group2. batchid2 must either have null intersection with batchid1, or be exactly equal to batchid1. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells from group1 and group2 are decoded on each group in group1 and group2, respectively.fdr_target (
float
(default:0.05
)) – Tag features as DE based on posterior expected false discovery rate.silent (
bool
(default:False
)) – If True, disables the progress bar. Default: False.**kwargs – Keyword args for
scvi.model.base.DifferentialComputation.get_bayes_factors()
- Return type:
- Returns:
Differential expression DataFrame.
get_anndata_manager
- SCVI.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 anAnnDataManager
specific to this model instance.
get_elbo
- SCVI.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 (
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.
- Return type:
get_feature_correlation_matrix
- SCVI.get_feature_correlation_matrix(adata=None, indices=None, n_samples=10, batch_size=64, rna_size_factor=1000, transform_batch=None, correlation_type='spearman')[source]#
Generate gene-gene correlation matrix using scvi uncertainty and expression.
- 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.n_samples (
int
(default:10
)) – Number of posterior samples to use for estimation.batch_size (
int
(default:64
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.rna_size_factor (
int
(default:1000
)) – size factor for RNA prior to sampling gamma distribution.transform_batch (
Optional
[Sequence
[Union
[int
,float
,str
]]] (default:None
)) –Batches to condition on. If transform_batch is:
None, then real observed batch is used.
int, then batch transform_batch is used.
list of int, then values are averaged over provided batches.
correlation_type (
Literal
[‘spearman’, ‘pearson’] (default:'spearman'
)) – One of “pearson”, “spearman”.
- Return type:
- Returns:
Gene-gene correlation matrix
get_from_registry
- SCVI.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.
get_latent_library_size
- SCVI.get_latent_library_size(adata=None, indices=None, give_mean=True, batch_size=None)[source]#
Returns the latent library size for each cell.
This is denoted as \(\ell_n\) in the scVI paper.
- 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.give_mean (
bool
(default:True
)) – Return the mean or a sample from the posterior distribution.batch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
- Return type:
get_latent_representation
- SCVI.get_latent_representation(adata=None, indices=None, give_mean=True, mc_samples=5000, batch_size=None, return_dist=False)[source]#
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.give_mean (
bool
(default:True
)) – Give mean of distribution or sample from it.mc_samples (
int
(default:5000
)) – For distributions with no closed-form mean (e.g., logistic normal), how many Monte Carlo samples to take for computing mean.batch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.return_dist (
bool
(default:False
)) – Return the distribution parameters of the latent variables rather than their sampled values. If True, ignores give_mean and mc_samples.
- Return type:
- Returns:
Low-dimensional representation for each cell or a tuple containing its mean and variance.
get_likelihood_parameters
- SCVI.get_likelihood_parameters(adata=None, indices=None, n_samples=1, give_mean=False, batch_size=None)[source]#
Estimates for the parameters of the likelihood \(p(x \mid z)\)
- 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.n_samples (
Optional
[int
] (default:1
)) – Number of posterior samples to use for estimation.give_mean (
Optional
[bool
] (default:False
)) – Return expected value of parameters or a samplesbatch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
- Return type:
get_marginal_ll
- SCVI.get_marginal_ll(adata=None, indices=None, n_mc_samples=1000, batch_size=None)[source]#
Return the marginal LL for the data.
The computation here is a biased estimator of the marginal log likelihood of the data. Note, this is not the negative log likelihood, higher is better.
- 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.n_mc_samples (
int
(default:1000
)) – Number of Monte Carlo samples to use for marginal LL estimation.batch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
- Return type:
get_normalized_expression
- SCVI.get_normalized_expression(adata=None, indices=None, transform_batch=None, gene_list=None, library_size=1, n_samples=1, n_samples_overall=None, batch_size=None, return_mean=True, return_numpy=None)[source]#
Returns the normalized (decoded) gene expression.
This is denoted as \(\rho_n\) in the scVI paper.
- 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.transform_batch (
Optional
[Sequence
[Union
[int
,float
,str
]]] (default:None
)) –Batch to condition on. If transform_batch is:
None, then real observed batch is used.
int, then batch transform_batch is used.
gene_list (
Optional
[Sequence
[str
]] (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 (
Union
[float
,Literal
[‘latent’]] (default:1
)) – 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 (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.return_mean (
bool
(default:True
)) – Whether to return the mean of the samples.return_numpy (
Optional
[bool
] (default:None
)) – Return andarray
instead of aDataFrame
. 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:
- 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.
get_reconstruction_error
- SCVI.get_reconstruction_error(adata=None, indices=None, batch_size=None)[source]#
Return the reconstruction error for the data.
This is typically written as \(p(x \mid z)\), the likelihood term given one posterior sample. Note, this is not the negative likelihood, higher is better.
- 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.
- Return type:
load
- classmethod SCVI.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_query_data
- classmethod SCVI.load_query_data(adata, reference_model, inplace_subset_query_vars=False, use_gpu=None, unfrozen=False, freeze_dropout=False, freeze_expression=True, freeze_decoder_first_layer=True, freeze_batchnorm_encoder=True, freeze_batchnorm_decoder=False, freeze_classifier=True)[source]#
Online update of a reference model with scArches algorithm [Lotfollahi et al., 2021].
- Parameters:
adata (
AnnData
) – 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 theregistry
.reference_model (
Union
[str
,BaseModelClass
]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.inplace_subset_query_vars (
bool
(default:False
)) – Whether to subset and rearrange query vars inplace based on vars used to train reference 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).unfrozen (
bool
(default:False
)) – Override all other freeze options for a fully unfrozen modelfreeze_dropout (
bool
(default:False
)) – Whether to freeze dropout during trainingfreeze_expression (
bool
(default:True
)) – Freeze neurons corersponding to expression in first layerfreeze_decoder_first_layer (
bool
(default:True
)) – Freeze neurons corersponding to first layer in decoderfreeze_batchnorm_encoder (
bool
(default:True
)) – Whether to freeze batchnorm weight and bias during training for encoderfreeze_batchnorm_decoder (
bool
(default:False
)) – Whether to freeze batchnorm weight and bias during training for decoderfreeze_classifier (
bool
(default:True
)) – Whether to freeze classifier completely. Only applies to SCANVI.
load_registry
- static SCVI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
posterior_predictive_sample
- SCVI.posterior_predictive_sample(adata=None, indices=None, n_samples=1, gene_list=None, batch_size=None)[source]#
Generate observation samples from the posterior predictive distribution.
The posterior predictive distribution is written as \(p(\hat{x} \mid x)\).
- 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.n_samples (
int
(default:1
)) – Number of samples for each cell.gene_list (
Optional
[Sequence
[str
]] (default:None
)) – Names of genes of interest.batch_size (
Optional
[int
] (default:None
)) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.
- Return type:
- Returns:
x_new :
torch.Tensor
tensor with shape (n_cells, n_genes, n_samples)
prepare_query_anndata
- static SCVI.prepare_query_anndata(adata, reference_model, return_reference_var_names=False, inplace=True)[source]#
Prepare data for query integration.
This function will return a new AnnData object with padded zeros for missing features, as well as correctly sorted features.
- Parameters:
adata (
AnnData
) – 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 theregistry
.reference_model (
Union
[str
,BaseModelClass
]) – Either an already instantiated model of the same class, or a path to saved outputs for reference model.return_reference_var_names (
bool
(default:False
)) – Only load and return reference var names if True.inplace (
bool
(default:True
)) – Whether to subset and rearrange query vars inplace or return new AnnData.
- Return type:
- Returns:
Query adata ready to use in load_query_data unless return_reference_var_names in which case a pd.Index of reference var names is returned.
register_manager
- classmethod SCVI.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 thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
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 previousAnnDataManager
.
save
- SCVI.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 anndataanndata_write_kwargs – Kwargs for
write()
setup_anndata
- classmethod SCVI.setup_anndata(adata, layer=None, batch_key=None, labels_key=None, size_factor_key=None, categorical_covariate_keys=None, continuous_covariate_keys=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:
layer (
Optional
[str
] (default:None
)) – if not None, uses this as the key in adata.layers for raw count data.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.size_factor_key (
Optional
[str
] (default:None
)) – key in adata.obs for size factor information. Instead of using library size as a size factor, the provided size factor column will be used as offset in the mean of the likelihood. Assumed to be on linear scale.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.
- Sets up the
to_device
- SCVI.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
- SCVI.to_latent_mode(mode='dist', use_latent_qzm_key='X_latent_qzm', use_latent_qzv_key='X_latent_qzv')[source]#
Put the model into latent mode.
The model is put into latent mode by registering new anndata fields required for latent mode support - latent qzm, latent qzv, and adata uns containing latent mode type - 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:
train
- SCVI.train(max_epochs=None, use_gpu=None, train_size=0.9, validation_size=None, batch_size=128, early_stopping=False, plan_kwargs=None, **trainer_kwargs)[source]#
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, e.g., ‘cuda:0’), 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.early_stopping (
bool
(default:False
)) – Perform early stopping. Additional arguments can be passed in **kwargs. SeeTrainer
for further options.plan_kwargs (
Optional
[dict
] (default:None
)) – Keyword args forTrainingPlan
. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- SCVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#
Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args