linear_model.ZINB_grad.train_ZINB_with_val

linear_model.ZINB_grad.train_ZINB_with_val(x, val_data, optimizer, model, device, X_val=None, epochs=150, PATH='/home/longlab/Data/Thesis/Data/', early_stop=False)

Trains a ZINB-Grad model with validation.

The function will train a ZINB-Grad model with validation using an optimizer for a number of epochs, and it will return losses, negative log-likelihood, and validation

losses which were obtained during the training procedure.

The function will save the model with the best validation loss, and it uses early stopping to avoid overfitting. In the early stopping the model with the best validation loss will be loaded.

Parameters

xtorch.Tensor

It is the data for training, a Tensor of shape (n_samples, n_features).

val_datatorch.Tensor

It is the validation data, a Tensor of shape (n_samples_val, n_features).

optimizer: An object of torch.optim.Optimizer

For more details, please refer to Pytorch documentation.

model: An object of the ZINB_Grad class

Please refer to the example.

deviceA torch.device object

Please refer to Pytorch documentation for more details.

X_valtorch.Tensor (optional, default=None)

It is the X parameter of the ZINB-Grad model for the validation samples, a Tensor of shape (n_samples_val, M).

epochsint (optional, default=150)

Number of iteration for training.

early_stopbool (optional, default=False)

If True the function will use early stopping.

PATHstr

The path to save the best model.

Returns

losseslist

A list consisting of the loss of each epoch.

neg_log_likslist

A list consisting of the negative Log-likelihood of each epoch.

val_losseslist

A list consisting of the validation losses of each validation step.