tune_race_anova()
computes a set of performance metrics (e.g. accuracy or RMSE)
for a predefined set of tuning parameters that correspond to a model or
recipe across one or more resamples of the data. After an initial number of
resamples have been evaluated, the process eliminates tuning parameter
combinations that are unlikely to be the best results using a repeated
measure ANOVA model.
tune_race_anova(object, ...) # S3 method for model_spec tune_race_anova( object, preprocessor, resamples, ..., param_info = NULL, grid = 10, metrics = NULL, control = control_race() ) # S3 method for workflow tune_race_anova( object, resamples, ..., param_info = NULL, grid = 10, metrics = NULL, control = control_race() )
object  A 

...  Not currently used. 
preprocessor  A traditional model formula or a recipe created using

resamples  An 
param_info  A 
grid  A data frame of tuning combinations or a positive integer. The data frame should have columns for each parameter being tuned and rows for tuning parameter candidates. An integer denotes the number of candidate parameter sets to be created automatically. 
metrics  A 
control  An object used to modify the tuning process. See

The technical details of this method are described in Kuhn (2014).
Racing methods are efficient approaches to grid search. Initially, the
function evaluates all tuning parameters on a small initial set of
resamples. The burn_in
argument of control_race()
sets the number of
initial resamples.
The performance statistics from these resamples are analyzed to determine which tuning parameters are not statistically different from the current best setting. If a parameter is statistically different, it is excluded from further resampling.
The next resample is used with the remaining parameter combinations and the statistical analysis is updated. More candidate parameters may be excluded with each new resample that is processed.
This function determines statistical significance using a repeated measures ANOVA
model where the performance statistic (e.g., RMSE, accuracy, etc.) is the
outcome data and the random effect is due to resamples. The
control_race()
function contains are parameter for the significance cutoff
applied to the ANOVA results as well as other relevant arguments.
There is benefit to using racing methods in conjunction with parallel processing. The following section shows a benchmark of results for one dataset and model.
To demonstrate, we use a SVM model with the kernlab
package.
library(kernlab) library(tidymodels) library(finetune) library(doParallel) ##  data(cells, package = "modeldata") cells < cells %>% select(case) ##  set.seed(6376) rs < bootstraps(cells, times = 25)
We’ll only tune the model parameters (i.e., not recipe tuning):
##  svm_spec < svm_rbf(cost = tune(), rbf_sigma = tune()) %>% set_engine("kernlab") %>% set_mode("classification") svm_rec < recipe(class ~ ., data = cells) %>% step_YeoJohnson(all_predictors()) %>% step_normalize(all_predictors()) svm_wflow < workflow() %>% add_model(svm_spec) %>% add_recipe(svm_rec) set.seed(1) svm_grid < svm_spec %>% parameters() %>% grid_latin_hypercube(size = 25)
We’ll get the times for grid search and ANOVA racing with and without parallel processing:
##  ## Regular grid search system.time({ set.seed(2) svm_wflow %>% tune_grid(resamples = rs, grid = svm_grid) })
## user system elapsed ## 741.660 19.654 761.357
##  ## With racing system.time({ set.seed(2) svm_wflow %>% tune_race_anova(resamples = rs, grid = svm_grid) })
## user system elapsed ## 133.143 3.675 136.822
Speedup of 5.56fold for racing.
##  ## Parallel processing setup cores < parallel::detectCores(logical = FALSE) cores
## [1] 10
cl < makePSOCKcluster(cores) registerDoParallel(cl)
##  ## Parallel grid search system.time({ set.seed(2) svm_wflow %>% tune_grid(resamples = rs, grid = svm_grid) })
## user system elapsed ## 1.112 0.190 126.650
Parallel processing with grid search was 6.01fold faster than sequential grid search.
##  ## Parallel racing system.time({ set.seed(2) svm_wflow %>% tune_race_anova(resamples = rs, grid = svm_grid) })
## user system elapsed ## 1.908 0.261 21.442
Parallel processing with racing was 35.51fold faster than sequential grid search.
There is a compounding effect of racing and parallel processing but its magnitude depends on the type of model, number of resamples, number of tuning parameters, and so on.
Kuhn, M 2014. "Futility Analysis in the CrossValidation of Machine Learning Models." https://arxiv.org/abs/1405.6974.
#>#> #>#>#> #>##  data(two_class_dat, package = "modeldata") set.seed(6376) rs < bootstraps(two_class_dat, times = 10) ##  # optimize an regularized discriminant analysis model rda_spec < discrim_regularized(frac_common_cov = tune(), frac_identity = tune()) %>% set_engine("klaR") ##  ctrl < control_race(verbose_elim = TRUE) set.seed(11) grid_anova < rda_spec %>% tune_race_anova(Class ~ ., resamples = rs, grid = 10, control = ctrl)#>#> #>#>#> #>#>#> #>#> #>#>#> #>#>#>#> #>#>#> #>#> #>#>#> #> #> #>#> #>#>#> #>#>#> #>#>#> #>#>#> #>#>#> ℹ Resamples are analyzed in a random order.#> ℹ Bootstrap05: All but one parameter combination were eliminated.# Shows only the fully resampled parameters show_best(grid_anova, metric = "roc_auc", n = 2)#> # A tibble: 1 x 8 #> frac_common_cov frac_identity .metric .estimator mean n std_err .config #> <dbl> <dbl> <chr> <chr> <dbl> <int> <dbl> <chr> #> 1 0.0691 0.0437 roc_auc binary 0.886 10 0.00513 Preproce…# }