AutoZIVAE¶
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class
scvi.models.AutoZIVAE(n_input, alpha_prior=0.5, beta_prior=0.5, minimal_dropout=0.01, zero_inflation='gene', **args)[source]¶ Bases:
scvi.models.vae.VAEAutoZI variational auto-encoder model.
Implementation of AutoZI model [Clivio19].
- n_input
Number of input genes
- alpha_prior
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 not ``None’’, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.
- beta_prior
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 not ``None’’, otherwise the prior Beta distribution will be learned on an Empirical Bayes fashion.
- minimal_dropout
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: 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
See VAE docstring (scvi/models/vae.py) for more parameters.
reconstruction_lossshould not be specified.Examples
>>> gene_dataset = CortexDataset() >>> autozivae = AutoZIVAE(gene_dataset.nb_genes, alpha_prior=0.5, beta_prior=0.5, minimal_dropout=0.01)
Methods Summary
cuda([device])Moves all model parameters and also fixed prior alpha and beta values, when relevant, to the GPU.
forward(x, local_l_mean, local_l_var[, …])Returns the reconstruction loss and the Kullback divergences
get_alphas_betas([as_numpy])get_reconstruction_loss(x, px_rate, px_r, …)inference(x[, batch_index, y, n_samples, …])Helper function used in forward pass
rescale_dropout(px_dropout[, eps_log])reshape_bernoulli(bernoulli_params[, …])sample_bernoulli_params([batch_index, y, …])sample_from_beta_distribution(alpha, beta[, …])Methods Documentation
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cuda(device=None)[source]¶ Moves all model parameters and also fixed prior alpha and beta values, when relevant, to the GPU.
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forward(x, local_l_mean, local_l_var, batch_index=None, y=None)[source]¶ Returns the reconstruction loss and the Kullback divergences
- Parameters
x (
TensorTensor) – tensor of values with shape (batch_size, n_input)local_l_mean (
TensorTensor) – tensor of means of the prior distribution of latent variable l with shape (batch_size, 1)local_l_var (
TensorTensor) – tensor of variancess of the prior distribution of latent variable l with shape (batch_size, 1)batch_index (
Tensor,NoneOptional[Tensor]) – array that indicates which batch the cells belong to with shapebatch_sizey (
Tensor,NoneOptional[Tensor]) – tensor of cell-types labels with shape (batch_size, n_labels)
- Return type
- Returns
2-tuple of
torch.FloatTensorthe reconstruction loss and the Kullback divergences
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get_reconstruction_loss(x, px_rate, px_r, px_dropout, bernoulli_params, eps_log=1e-08, **kwargs)[source]¶