Plot training loss history for a neuralGAM model

This function visualizes the training and/or validation loss at the end of each backfitting iteration for each term-specific model in a fitted neuralGAM object. It is designed to work with the history component of a trained neuralGAM model.

plot_history(model, select = NULL, metric = c("loss", "val_loss"))

Arguments

model: A fitted neuralGAM model.
select: Optional character vector of term names (e.g. "x1" or c("x1", "x3")) to subset the history. If NULL (default), all terms are included.
metric: Character vector indicating which loss metric(s) to plot. Options are "loss", "val_loss", or both. Defaults to both.

Value

A ggplot object showing the loss curves by backfitting iteration, with facets per term.

Author

Ines Ortega-Fernandez, Marta Sestelo

Examples

# \dontrun{
  set.seed(123)
  n <- 200
  x1 <- runif(n, -2, 2)
  x2 <- runif(n, -2, 2)
  y <- 2 + x1^2 + sin(x2) + rnorm(n, 0, 0.1)
  df <- data.frame(x1 = x1, x2 = x2, y = y)

  model <- neuralGAM::neuralGAM(
    y ~ s(x1) + s(x2),
    data = df,
    num_units = 8,
    family = "gaussian",
    max_iter_backfitting = 2,
    max_iter_ls = 1,
    learning_rate = 0.01,
    seed = 42,
    validation_split = 0.2,
    verbose = 0
  )

  plot_history(model)                      # Plot all terms

  plot_history(model, select = "x1")       # Plot just x1

  plot_history(model, metric = "val_loss") # Plot only validation loss

# }