Generic Trainer for torch models

An enhanced R6 trainer mirroring PyHealth's Python version. It supports:

• Dynamic steps_per_epoch: can iterate indefinitely over a dataloader to reach a target number of steps, just like Python.

• Parameter‑group–wise weight decay: bias and LayerNorm parameters are excluded from L2 regularisation.

• Gradient clipping.

• Optional progress bar using progressr::progressor() (falls back to simple logging).

• Correctly named additional_outputs collection.

Public fields

model

A torch model object.

metrics

A list of metric names.

device

The computation device ("cpu" or "cuda").

exp_path

Path to save logs and checkpoints.

Methods

Public methods

• Trainer$new()

• Trainer$train()

• Trainer$inference()

• Trainer$evaluate()

• Trainer$save_ckpt()

• Trainer$load_ckpt()

• Trainer$clone()

---

Method new()

Initialize the Trainer.

Usage

Trainer$new(
  model,
  checkpoint_path = NULL,
  metrics = NULL,
  device = NULL,
  enable_logging = TRUE,
  output_path = NULL,
  exp_name = NULL
)

Arguments

model

A torch model.

checkpoint_path

Optional checkpoint path to load.

metrics

List of metric names.

device

Computation device.

enable_logging

Whether to enable file logging.

output_path

Output directory.

exp_name

Optional experiment name.

---

Method train()

Train the model.

Usage

Trainer$train(
  train_dataloader,
  val_dataloader = NULL,
  test_dataloader = NULL,
  epochs = 5,
  optimizer_class = optim_adam,
  optimizer_params = list(lr = 0.001),
  steps_per_epoch = NULL,
  evaluation_steps = 1L,
  weight_decay = 0,
  max_grad_norm = NULL,
  monitor = NULL,
  monitor_criterion = "max",
  load_best_model_at_last = TRUE,
  use_progress_bar = TRUE
)

Arguments

train_dataloader

Training dataloader.

val_dataloader

Optional validation dataloader.

test_dataloader

Optional test dataloader.

epochs

Number of training epochs.

optimizer_class

Optimizer constructor.

optimizer_params

Parameters for optimizer.

steps_per_epoch

Optional override for steps per epoch.

evaluation_steps

Steps between evaluations.

weight_decay

Weight decay parameter.

max_grad_norm

Optional gradient clipping norm.

monitor

Metric name to monitor.

monitor_criterion

"max" or "min".

load_best_model_at_last

Load best model after training.

use_progress_bar

Show training progress.

---

Method inference()

Perform inference on a dataloader.

Usage

Trainer$inference(
  dataloader,
  additional_outputs = NULL,
  return_patient_ids = FALSE,
  use_progress_bar = FALSE
)

Arguments

dataloader

A dataloader.

additional_outputs

Vector of additional outputs to capture.

return_patient_ids

Whether to return patient IDs.

use_progress_bar

Whether to show a progress bar.

---

Method evaluate()

Evaluate the model using a dataloader.

Usage

Trainer$evaluate(dataloader, use_progress_bar = FALSE)

Arguments

dataloader

A dataloader to evaluate on.

use_progress_bar

Whether to show a progress bar.

---

Method save_ckpt()

Save model checkpoint.

Usage

Trainer$save_ckpt(path)

Arguments

path

File path to save checkpoint.

---

Method load_ckpt()

Load model checkpoint.

Usage

Trainer$load_ckpt(path)

Arguments

path

File path to load checkpoint from.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Trainer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.