Validate the training controls

Every one of these decides how the split, the batches or the restart loop is built, and an unchecked value is reported by whichever base function hits it first: batch_size = 0 comes back as "invalid '(to - from)/by'", validation_fraction = 1 as "wrong sign in 'by' argument", and n_restarts = 0 as "Training failed: no restart produced a finite validation loss", which blames training for an argument. npe() and nle() call this before they simulate, so a typo does not cost the budget first.

Usage

check_train_controls(
  max_epochs,
  batch_size,
  lr,
  validation_fraction,
  patience,
  n_restarts,
  clip_grad_norm,
  n = NULL,
  min_val_rows = 1L
)

Arguments

max_epochs, batch_size, lr, validation_fraction, patience

Neural training controls (Adam optimizer, early stopping on validation loss). The defaults (batch_size = 200, lr = 5e-4, validation_fraction = 0.1, patience = 20) match Python sbi; max_epochs is a high guard cap that early stopping normally reaches first.

n_restarts

Train this many independently initialized networks and keep the one with the best validation loss (guards against bad initializations and MDN mode collapse).

clip_grad_norm

Maximum gradient norm during training (Inf disables clipping). The learning rate also decays 2x after 10 epochs without validation improvement.

n

Number of training rows, or NULL when they do not exist yet.

min_val_rows

Smallest validation split this call will accept (default 1L, i.e. only require it non-empty). Raise it for an objective that needs more than one validation row to produce a real signal.

Details

n is optional because that call happens before there are any rows. When it is known, validation_fraction is checked against it: the requirement is that both sides of the split come out non-empty, which the fraction alone cannot decide. min_val_rows raises that floor for callers whose objective needs more than one validation row to mean anything – see fit_nre_net().