scvi.model.AUTOZI#
- class scvi.model.AUTOZI(adata, n_hidden=128, n_latent=10, n_layers=1, dropout_rate=0.1, dispersion='gene', latent_distribution='normal', alpha_prior=0.5, beta_prior=0.5, minimal_dropout=0.01, zero_inflation='gene', use_observed_lib_size=True, **model_kwargs)[source]#
Automatic identification of ZI genes [Clivio et al., 2019].
- 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 NN
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
latent_distribution (Literal['normal', 'ln']) –
One of the following:
'normal'
- Normal distribution'ln'
- Logistic normal distribution (Normal(0, I) transformed by softmax)
alpha_prior (Optional[float]) – Float denoting the alpha parameter of the prior Beta distribution of the zero-inflation Bernoulli parameter. Should be between 0 and 1, not included. When set to
None
, will be set to 1 - beta_prior if beta_prior is notNone
, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.beta_prior (Optional[float]) – Float denoting the beta parameter of the prior Beta distribution of the zero-inflation Bernoulli parameter. Should be between 0 and 1, not included. When set to
None
, will be set to 1 - alpha_prior if alpha_prior is notNone
, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.minimal_dropout (float) – Float denoting the lower bound of the cell-gene ZI rate in the ZINB component. Must be non-negative. Can be set to 0 but not recommended as this may make the mixture problem ill-defined.
zero_inflation (str) –
One of the following:
'gene'
- zero-inflation Bernoulli parameter of AutoZI is constant per gene across cells'gene-batch'
- zero-inflation Bernoulli parameter can differ between different batches'gene-label'
- zero-inflation Bernoulli parameter can differ between different labels'gene-cell'
- zero-inflation Bernoulli parameter can differ for every gene in every cell
use_observed_lib_size (bool) – Use observed library size for RNA as scaling factor in mean of conditional distribution
**model_kwargs – Keyword args for
AutoZIVAE
Examples
>>> adata = anndata.read_h5ad(path_to_anndata) >>> scvi.model.AUTOZI.setup_anndata(adata, batch_key="batch") >>> vae = scvi.model.AUTOZI(adata) >>> vae.train(n_epochs=400)
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. |
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Whether the model has been trained. |
|
Observations that are in test set. |
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Observations that are in train set. |
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Observations that are in validation set. |
Methods table#
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Converts a legacy saved model (<v0.15.0) to the updated save format. |
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Return parameters of Bernoulli Beta distributions in a dictionary. |
|
Retrieves the |
|
Return the ELBO for the data. |
|
Returns the object in AnnData associated with the key in the data registry. |
|
Return the latent representation for each cell. |
|
Return the marginal LL for the data. |
|
Return the reconstruction error for the data. |
|
Instantiate a model from the saved output. |
|
Return the full registry saved with the model. |
|
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
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod AUTOZI.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.
get_alphas_betas
- AUTOZI.get_alphas_betas(as_numpy=True)[source]#
Return parameters of Bernoulli Beta distributions in a dictionary.
get_anndata_manager
- AUTOZI.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
- AUTOZI.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_from_registry
- AUTOZI.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_representation
- AUTOZI.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_marginal_ll
- AUTOZI.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_reconstruction_error
- AUTOZI.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 AUTOZI.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_registry
- static AUTOZI.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
register_manager
- classmethod AUTOZI.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
- AUTOZI.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 AUTOZI.setup_anndata(adata, batch_key=None, labels_key=None, 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.
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 (Optional[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.layer (Optional[str]) – if not
None
, uses this as the key inadata.layers
for raw count data.
to_device
- AUTOZI.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
- AUTOZI.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]) – Number of passes through the dataset. If
None
, defaults tonp.min([round((20000 / n_cells) * 400), 400])
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).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.
early_stopping (bool) – Perform early stopping. Additional arguments can be passed in
**kwargs
. SeeTrainer
for further options.plan_kwargs (Optional[dict]) – Keyword args for
TrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.**trainer_kwargs – Other keyword args for
Trainer
.
view_anndata_setup
- AUTOZI.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