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
  )
#> Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session
#> Error in tensorflow::set_random_seed(seed): TensorFlow not installed, please run `tensorflow::install_tensorflow()`

  plot_history(model)                      # Plot all terms
#> Error: object 'model' not found
  plot_history(model, select = "x1")       # Plot just x1
#> Error: object 'model' not found
  plot_history(model, metric = "val_loss") # Plot only validation loss
#> Error: object 'model' not found
# }