Tune VAEReg on a train/validation split

Tune VAEReg on a train/validation split

Usage

tune_vae_train_val(X_tr, y_tr, X_va, y_va, seed = 123, grid_vae)

Arguments

X_tr

Train predictors matrix.

y_tr

Train response numeric.

X_va

Validation predictors matrix.

y_va

Validation response numeric.

seed

Integer seed.

grid_vae

Data frame with required columns: hidden_enc (list), hidden_dec (list), latent_dim, dropout, lr, beta_kl, alpha_y, epochs, batch_size, patience.

Value

A list with grid (input grid) and tuning_df (metrics per config).

Examples

if (FALSE) { # \dontrun{
  vae_configure()
  X <- matrix(rnorm(500), nrow = 50, ncol = 10)
  y <- rnorm(50)
  grid <- data.frame(
    latent_dim = 4L, dropout = 0.1, lr = 0.001,
    beta_kl = 1, alpha_y = 1, epochs = 5L,
    batch_size = 16L, patience = 3L
  )
  grid$hidden_enc <- list(c(32L, 16L))
  grid$hidden_dec <- list(c(16L, 32L))
  result <- tune_vae_train_val(
    X_tr = X[1:40, ], y_tr = y[1:40],
    X_va = X[41:50, ], y_va = y[41:50],
    grid_vae = grid
  )
} # }