ClassifierTrainer

class scvi.inference.ClassifierTrainer(*args, train_size=0.9, test_size=None, sampling_model=None, sampling_zl=False, use_cuda=True, **kwargs)

Bases: scvi.inference.trainer.Trainer

Class for training a classifier either on the raw data or on top of the latent space of another model.

Parameters

• model – A model instance from class VAE, VAEC, SCANVI

• gene_dataset – A gene_dataset instance like CortexDataset()

• train_size – The train size, a float between 0 and 1 representing proportion of dataset to use for training to use Default: 0.9.

• test_size – The test size, a float between 0 and 1 representing proportion of dataset to use for testing to use Default: None.

• sampling_model – Model with z_encoder with which to first transform data.

• sampling_zl – Transform data with sampling_model z_encoder and l_encoder and concat.

• **kwargs – Other keywords arguments from the general Trainer class.

Examples

>>> gene_dataset = CortexDataset()
>>> vae = VAE(gene_dataset.nb_genes, n_batch=gene_dataset.n_batches * False,
... n_labels=gene_dataset.n_labels)

>>> classifier = Classifier(vae.n_latent, n_labels=cortex_dataset.n_labels)
>>> trainer = ClassifierTrainer(classifier, gene_dataset, sampling_model=vae, train_size=0.5)
>>> trainer.train(n_epochs=20, lr=1e-3)
>>> trainer.test_set.accuracy()

Attributes Summary

posteriors_loop

Methods Summary

compute_predictions([soft])	param soft (Default value = False)
loss(tensors_labelled)

Attributes Documentation

posteriors_loop

Methods Documentation

compute_predictions(soft=False)

Parameters

soft – (Default value = False)

Returns

the true labels and the predicted labels

loss(tensors_labelled)