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Control aspects of the simulated annealing search process


  verbose = FALSE,
  verbose_iter = TRUE,
  no_improve = Inf,
  restart = 8L,
  radius = c(0.05, 0.15),
  flip = 3/4,
  cooling_coef = 0.02,
  extract = NULL,
  save_pred = FALSE,
  time_limit = NA,
  pkgs = NULL,
  save_workflow = FALSE,
  save_history = FALSE,
  event_level = "first",
  parallel_over = NULL,
  allow_par = TRUE,
  backend_options = NULL



A logical for logging results (other than warnings and errors, which are always shown) as they are generated during training in a single R process. When using most parallel backends, this argument typically will not result in any logging. If using a dark IDE theme, some logging messages might be hard to see; try setting the tidymodels.dark option with options(tidymodels.dark = TRUE) to print lighter colors.


A logical for logging results of the search process. Defaults to FALSE. If using a dark IDE theme, some logging messages might be hard to see; try setting the tidymodels.dark option with options(tidymodels.dark = TRUE) to print lighter colors.


The integer cutoff for the number of iterations without better results.


The number of iterations with no improvement before new tuning parameter candidates are generated from the last, overall best conditions.


Two real numbers on (0, 1) describing what a value "in the neighborhood" of the current result should be. If all numeric parameters were scaled to be on the [0, 1] scale, these values set the min. and max. of a radius of a circle used to generate new numeric parameter values.


A real number between [0, 1] for the probability of changing any non-numeric parameter values at each iteration.


A real, positive number to influence the cooling schedule. Larger values decrease the probability of accepting a sub-optimal parameter setting.


An optional function with at least one argument (or NULL) that can be used to retain arbitrary objects from the model fit object, recipe, or other elements of the workflow.


A logical for whether the out-of-sample predictions should be saved for each model evaluated.


A number for the minimum number of minutes (elapsed) that the function should execute. The elapsed time is evaluated at internal checkpoints and, if over time, the results at that time are returned (with a warning). This means that the time_limit is not an exact limit, but a minimum time limit.


An optional character string of R package names that should be loaded (by namespace) during parallel processing.


A logical for whether the workflow should be appended to the output as an attribute.


A logical to save the iteration details of the search. These are saved to tempdir() named sa_history.RData. These results are deleted when the R session ends. This option is only useful for teaching purposes.


A single string containing either "first" or "second". This argument is passed on to yardstick metric functions when any type of class prediction is made, and specifies which level of the outcome is considered the "event".


A single string containing either "resamples" or "everything" describing how to use parallel processing. Alternatively, NULL is allowed, which chooses between "resamples" and "everything" automatically.

If "resamples", then tuning will be performed in parallel over resamples alone. Within each resample, the preprocessor (i.e. recipe or formula) is processed once, and is then reused across all models that need to be fit.

If "everything", then tuning will be performed in parallel at two levels. An outer parallel loop will iterate over resamples. Additionally, an inner parallel loop will iterate over all unique combinations of preprocessor and model tuning parameters for that specific resample. This will result in the preprocessor being re-processed multiple times, but can be faster if that processing is extremely fast.

If NULL, chooses "resamples" if there are more than one resample, otherwise chooses "everything" to attempt to maximize core utilization.

Note that switching between parallel_over strategies is not guaranteed to use the same random number generation schemes. However, re-tuning a model using the same parallel_over strategy is guaranteed to be reproducible between runs.


A logical to allow parallel processing (if a parallel backend is registered).


An object of class "tune_backend_options" as created by tune::new_backend_options(), used to pass arguments to specific tuning backend. Defaults to NULL for default backend options.


An object of class control_sim_anneal that echos the argument values.


#> Simulated annealing control object