scvi.model.base.UnsupervisedTrainingMixin#

class scvi.model.base.UnsupervisedTrainingMixin[source]#

General purpose unsupervised train method.

Methods table#

train([max_epochs, use_gpu, train_size, ...])

Train the model.

Methods#

train

UnsupervisedTrainingMixin.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 to np.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. If train_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. See Trainer for further options.

  • plan_kwargs (Optional[dict]) – Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

  • **trainer_kwargs – Other keyword args for Trainer.