control_grid: Control aspects of the grid search process

In tune: Tidy Tuning Tools

Control aspects of the grid search process

Description

Control aspects of the grid search process

Usage

control_grid(
  verbose = FALSE,
  allow_par = TRUE,
  extract = NULL,
  save_pred = FALSE,
  pkgs = NULL,
  save_workflow = FALSE,
  event_level = "first",
  parallel_over = NULL,
  backend_options = NULL
)

control_resamples(
  verbose = FALSE,
  allow_par = TRUE,
  extract = NULL,
  save_pred = FALSE,
  pkgs = NULL,
  save_workflow = FALSE,
  event_level = "first",
  parallel_over = NULL,
  backend_options = NULL
)

new_backend_options(..., class = character())

Arguments

verbose	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.
allow_par	A logical to allow parallel processing (if a parallel backend is registered).
extract	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.
save_pred	A logical for whether the out-of-sample predictions should be saved for each model evaluated.
pkgs	An optional character string of R package names that should be loaded (by namespace) during parallel processing.
save_workflow	A logical for whether the workflow should be appended to the output as an attribute.
event_level	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".
parallel_over	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.
backend_options	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.

Details

For extract, this function can be used to output the model object, the recipe (if used), or some components of either or both. When evaluated, the function's sole argument has a fitted workflow If the formula method is used, the recipe element will be NULL.

The results of the extract function are added to a list column in the output called .extracts. Each element of this list is a tibble with tuning parameter column and a list column (also called .extracts) that contains the results of the function. If no extraction function is used, there is no .extracts column in the resulting object. See tune_bayes() for more specific details.

Note that for collect_predictions(), it is possible that each row of the original data point might be represented multiple times per tuning parameter. For example, if the bootstrap or repeated cross-validation are used, there will be multiple rows since the sample data point has been evaluated multiple times. This may cause issues when merging the predictions with the original data.

control_resamples() is an alias for control_grid() and is meant to be used with fit_resamples().

Hyperparameters and extracted objects

When making use of submodels, tune can generate predictions and calculate metrics for multiple model .configurations using only one model fit. However, this means that if a function was supplied to a control function's extract argument, tune can only execute that extraction on the one model that was fitted. As a result, in the collect_extracts() output, tune opts to associate the extracted objects with the hyperparameter combination used to fit that one model workflow, rather than the hyperparameter combination of a submodel. In the output, this appears like a hyperparameter entry is recycled across many .config entries—this is intentional.

See https://parsnip.tidymodels.org/articles/Submodels.html to learn more about submodels.

tune documentation built on May 29, 2024, 7:32 a.m.