scvi.model.SCANVI#
- class scvi.model.SCANVI(adata, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', gene_likelihood='zinb', **model_kwargs)[source]#
Single-cell annotation using variational inference [Xu et al., 2021].
Inspired from M1 + M2 model, as described in (https://arxiv.org/pdf/1406.5298.pdf).
- Parameters:
adata (AnnData) – AnnData object that has been registered via
setup_anndata()
.n_hidden (int) – Number of nodes per hidden layer.
n_latent (int) – Dimensionality of the latent space.
n_layers (int) – Number of hidden layers used for encoder and decoder NNs.
dropout_rate (float) – Dropout rate for neural networks.
dispersion (Literal['gene', 'gene-batch', 'gene-label', 'gene-cell']) –
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']) –
One of:
'nb'
- Negative binomial distribution'zinb'
- Zero-inflated negative binomial distribution'poisson'
- Poisson distribution
**model_kwargs – Keyword args for
SCANVAE
Examples
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.SCANVI.setup_anndata(adata, batch_key="batch", labels_key="labels") >>> vae = scvi.model.SCANVI(adata, "Unknown") >>> vae.train() >>> adata.obsm["X_scVI"] = vae.get_latent_representation() >>> adata.obs["pred_label"] = vae.predict()
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 type of minified data associated with this model, if applicable. |
|
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. |
|
. |
|
Initialize scanVI model with weights from pretrained |
|
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. |
|
Minifies the model's adata. |
|
Generate observation samples from the posterior predictive distribution. |
|
Return cell label predictions. |
|
Prepare data for query integration. |
|
Registers an |
|
Save the state of the model. |
|
Sets up the |
|
Move model to device. |
|
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
minified_data_type
- SCANVI.minified_data_type[source]#
The type of minified data associated with this model, if applicable.
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod SCANVI.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.
differential_expression
- SCANVI.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].adata
AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.
- groupby
The key of the observations grouping to consider.
- group1
Subset of groups, e.g. [
'g1'
,'g2'
,'g3'
], to which comparison shall be restricted, or all groups ingroupby
(default).- group2
If
None
, compare each group ingroup1
to the union of the rest of the groups ingroupby
. If a group identifier, compare with respect to this group.- idx1
idx1
andidx2
can be used as an alternative to the AnnData keys. Custom identifier forgroup1
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 onadata.obs
, as described inpandas.DataFrame.query()
Ifidx1
is notNone
, this option overridesgroup1
andgroup2
.- idx2
Custom identifier for
group2
that has the same properties asidx1
. By default, includes all cells not specified inidx1
.- mode
Method for differential expression. See user guide for full explanation.
- delta
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
Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.- all_stats
Concatenate count statistics (e.g., mean expression group 1) to DE results.
- batch_correction
Whether to correct for batch effects in DE inference.
- batchid1
Subset of categories from
batch_key
registered insetup_anndata
, e.g. ['batch1'
,'batch2'
,'batch3'
], forgroup1
. Only used ifbatch_correction
isTrue
, and by default all categories are used.- batchid2
Same as
batchid1
for group2.batchid2
must either have null intersection withbatchid1
, or be exactly equal tobatchid1
. When the two sets are exactly equal, cells are compared by decoding on the same batch. When sets have null intersection, cells fromgroup1
andgroup2
are decoded on each group ingroup1
andgroup2
, respectively.- fdr_target
Tag features as DE based on posterior expected false discovery rate.
- silent
If True, disables the progress bar. Default: False.
- **kwargs
Keyword args for
scvi.model.base.DifferentialComputation.get_bayes_factors()
Differential expression DataFrame.
from_scvi_model
- classmethod SCANVI.from_scvi_model(scvi_model, unlabeled_category, labels_key=None, adata=None, **scanvi_kwargs)[source]#
Initialize scanVI model with weights from pretrained
SCVI
model.- Parameters:
scvi_model (SCVI) – Pretrained scvi model
labels_key (Optional[str]) – key in
adata.obs
for label information. Label categories can not be different if labels_key was used to setup the SCVI model. If None, uses thelabels_key
used to setup the SCVI model. If that was None, and error is raised.unlabeled_category (str) – Value used for unlabeled cells in
labels_key
used to setup AnnData with scvi.adata (Optional[AnnData]) – AnnData object that has been registered via
setup_anndata()
.scanvi_kwargs – kwargs for scANVI model
get_anndata_manager
- SCANVI.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
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_feature_correlation_matrix
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.n_samples (int) – Number of posterior samples to use for estimation.
batch_size (int) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.rna_size_factor (int) – size factor for RNA prior to sampling gamma distribution.
transform_batch (Optional[Sequence[Union[int, float, str]]]) –
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']) – One of “pearson”, “spearman”.
- Returns:
Gene-gene correlation matrix
- Return type:
get_from_registry
- SCANVI.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
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.give_mean (bool) – Return the mean or a sample from the posterior distribution.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_latent_representation
- SCANVI.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 typically denoted as \(z_n\).
- Parameters:
adata (Optional[AnnData]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.give_mean (bool) – Give mean of distribution or sample from it.
mc_samples (int) – 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]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_dist (bool) – Return (mean, variance) of distributions instead of just the mean. If
True
, ignoresgive_mean
andmc_samples
. In the case of the latter,mc_samples
is used to compute the mean of a transformed distribution. Ifreturn_dist
is true the untransformed mean and variance are returned.
- Returns:
Low-dimensional representation for each cell or a tuple containing its mean and variance.
- Return type:
get_likelihood_parameters
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.n_samples (Optional[int]) – Number of posterior samples to use for estimation.
give_mean (Optional[bool]) – Return expected value of parameters or a samples
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_marginal_ll
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.n_mc_samples (int) – Number of Monte Carlo samples to use for marginal LL estimation.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
get_normalized_expression
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.transform_batch (Optional[Sequence[Union[int, float, str]]]) –
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]]) – 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']]) – 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 library size.n_samples (int) – Number of posterior samples to use for estimation.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.return_mean (bool) – Whether to return the mean of the samples.
return_numpy (Optional[bool]) – Return a
ndarray
instead of aDataFrame
. DataFrame includes gene names as columns. If eithern_samples=1
orreturn_mean=True
, defaults toFalse
. Otherwise, it defaults toTrue
.n_samples_overall (int) –
- Returns:
If
n_samples
> 1 andreturn_mean
is False, then the shape is(samples, cells, genes)
. Otherwise, shape is(cells, genes)
. In this case, return type isDataFrame
unlessreturn_numpy
is True.- Return type:
get_reconstruction_error
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
load
- classmethod SCANVI.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 (Optional[Union[AnnData, MuData]]) – 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 (Optional[Union[str, int, bool]]) – 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).
backup_url (Optional[str]) – 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 SCANVI.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 the
registry
.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) – Whether to subset and rearrange query vars inplace based on vars used to train reference model.
use_gpu (Optional[Union[str, int, bool]]) – 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) – Override all other freeze options for a fully unfrozen model
freeze_dropout (bool) – Whether to freeze dropout during training
freeze_expression (bool) – Freeze neurons corersponding to expression in first layer
freeze_decoder_first_layer (bool) – Freeze neurons corersponding to first layer in decoder
freeze_batchnorm_encoder (bool) – Whether to freeze batchnorm weight and bias during training for encoder
freeze_batchnorm_decoder (bool) – Whether to freeze batchnorm weight and bias during training for decoder
freeze_classifier (bool) – Whether to freeze classifier completely. Only applies to
SCANVI
.
load_registry
- static SCANVI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
minify_adata
- SCANVI.minify_adata(minified_data_type='latent_posterior_parameters', use_latent_qzm_key='X_latent_qzm', use_latent_qzv_key='X_latent_qzv')[source]#
Minifies the model’s adata.
Minifies the adata, and registers new anndata fields: latent qzm, latent qzv, adata uns containing minified-adata type, and library size. This also sets the appropriate property on the module to indicate that the adata is minified.
- Parameters:
minified_data_type (Literal['latent_posterior_parameters']) –
How to minify the data. Currently only supports
latent_posterior_parameters
. If minified_data_type ==latent_posterior_parameters
:the original count data is removed (
adata.X
, adata.raw, and any layers)the parameters of the latent representation of the original data is stored
everything else is left untouched
use_latent_qzm_key (str) – Key to use in
adata.obsm
where the latent qzm params are storeduse_latent_qzv_key (str) – Key to use in
adata.obsm
where the latent qzv params are stored
Notes
The modification is not done inplace – instead the model is assigned a new (minified) version of the adata.
posterior_predictive_sample
- SCANVI.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]) – AnnData object with equivalent structure to initial AnnData. If
None
, defaults to the AnnData object used to initialize the model.indices (Optional[Sequence[int]]) – Indices of cells in adata to use. If
None
, all cells are used.n_samples (int) – Number of samples for each cell.
gene_list (Optional[Sequence[str]]) – Names of genes of interest.
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Returns:
x_new :
torch.Tensor
tensor with shape (n_cells, n_genes, n_samples)- Return type:
predict
- SCANVI.predict(adata=None, indices=None, soft=False, batch_size=None)[source]#
Return cell label predictions.
- Parameters:
adata (Optional[AnnData]) – AnnData object that has been registered via
setup_anndata()
.indices (Optional[Sequence[int]]) – Return probabilities for each class label.
soft (bool) – If True, returns per class probabilities
batch_size (Optional[int]) – Minibatch size for data loading into model. Defaults to
scvi.settings.batch_size
.
- Return type:
prepare_query_anndata
- static SCANVI.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 the
registry
.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) – Only load and return reference var names if True.
inplace (bool) – Whether to subset and rearrange query vars inplace or return new AnnData.
- Returns:
Query adata ready to use in
load_query_data
unlessreturn_reference_var_names
in which case a pd.Index of reference var names is returned.- Return type:
register_manager
- classmethod SCANVI.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
.- Parameters:
adata_manager (AnnDataManager) –
save
- SCANVI.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]) – Prefix to prepend to saved file names.
overwrite (bool) – Overwrite existing data or not. If
False
and directory already exists atdir_path
, error will be raised.save_anndata (bool) – If True, also saves the anndata
anndata_write_kwargs – Kwargs for
write()
setup_anndata
- classmethod SCANVI.setup_anndata(adata, labels_key, unlabeled_category, layer=None, batch_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:
adata (AnnData) – AnnData object. Rows represent cells, columns represent features.
layer (Optional[str]) – if not
None
, uses this as the key inadata.layers
for raw count data.batch_key (Optional[str]) – key in
adata.obs
for batch information. Categories will automatically be converted into integer categories and saved toadata.obs['_scvi_batch']
. IfNone
, assigns the same batch to all the data.labels_key (str) – key in
adata.obs
for label information. Categories will automatically be converted into integer categories and saved toadata.obs['_scvi_labels']
. IfNone
, assigns the same label to all the data.size_factor_key (Optional[str]) – 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]]) – 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]]) – 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.
to_device
- SCANVI.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
train
- SCANVI.train(max_epochs=None, n_samples_per_label=None, check_val_every_n_epoch=None, train_size=0.9, validation_size=None, batch_size=128, use_gpu=None, plan_kwargs=None, **trainer_kwargs)[source]#
Train the model.
- Parameters:
max_epochs (Optional[int]) – Number of passes through the dataset for semisupervised training.
n_samples_per_label (Optional[float]) – Number of subsamples for each label class to sample per epoch. By default, there is no label subsampling.
check_val_every_n_epoch (Optional[int]) – Frequency with which metrics are computed on the data for validation set for both the unsupervised and semisupervised trainers. If you’d like a different frequency for the semisupervised trainer, set check_val_every_n_epoch in semisupervised_train_kwargs.
train_size (float) – Size of training set in the range [0.0, 1.0].
validation_size (Optional[float]) – Size of the test set. If
None
, defaults to 1 -train_size
. Iftrain_size + validation_size < 1
, the remaining cells belong to a test set.batch_size (int) – Minibatch size to use during training.
use_gpu (Optional[Union[str, int, bool]]) – 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).plan_kwargs (Optional[dict]) – Keyword args for
SemiSupervisedTrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- SCANVI.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