metric_set() allows you to combine multiple metric functions together
into a new function that calculates all of them at once.
metric_set(...)
| ... | The bare names of the functions to be included in the metric set. |
|---|
All functions must be either:
Only numeric metrics
A mix of class metrics or class prob metrics
For instance, rmse() can be used with mae() because they
are numeric metrics, but not with accuracy() because it is a classification
metric. But accuracy() can be used with roc_auc().
The returned metric function will have a different argument list depending on whether numeric metrics or a mix of class/prob metrics were passed in.
# Numeric metric set signature: fn( data, truth, estimate, na_rm = TRUE, ... ) # Class / prob metric set signature: fn( data, truth, ..., estimate, estimator = NULL, na_rm = TRUE, event_level = yardstick_event_level() )
When mixing class and class prob metrics, pass in the hard predictions
(the factor column) as the named argument estimate, and the soft
predictions (the class probability columns) as bare column names or
tidyselect selectors to ....
library(dplyr) # Multiple regression metrics multi_metric <- metric_set(rmse, rsq, ccc) # The returned function has arguments: # fn(data, truth, estimate, na_rm = TRUE, ...) multi_metric(solubility_test, truth = solubility, estimate = prediction)#> # A tibble: 3 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 rmse standard 0.722 #> 2 rsq standard 0.879 #> 3 ccc standard 0.934# Groups are respected on the new metric function class_metrics <- metric_set(accuracy, kap) hpc_cv %>% group_by(Resample) %>% class_metrics(obs, estimate = pred)#> # A tibble: 20 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 accuracy multiclass 0.726 #> 2 Fold02 accuracy multiclass 0.712 #> 3 Fold03 accuracy multiclass 0.758 #> 4 Fold04 accuracy multiclass 0.712 #> 5 Fold05 accuracy multiclass 0.712 #> 6 Fold06 accuracy multiclass 0.697 #> 7 Fold07 accuracy multiclass 0.675 #> 8 Fold08 accuracy multiclass 0.721 #> 9 Fold09 accuracy multiclass 0.673 #> 10 Fold10 accuracy multiclass 0.699 #> 11 Fold01 kap multiclass 0.533 #> 12 Fold02 kap multiclass 0.512 #> 13 Fold03 kap multiclass 0.594 #> 14 Fold04 kap multiclass 0.511 #> 15 Fold05 kap multiclass 0.514 #> 16 Fold06 kap multiclass 0.486 #> 17 Fold07 kap multiclass 0.454 #> 18 Fold08 kap multiclass 0.531 #> 19 Fold09 kap multiclass 0.454 #> 20 Fold10 kap multiclass 0.492# --------------------------------------------------------------------------- # If you need to set options for certain metrics, # do so by wrapping the metric and setting the options inside the wrapper, # passing along truth and estimate as quoted arguments. # Then add on the function class of the underlying wrapped function, # and the direction of optimization. ccc_with_bias <- function(data, truth, estimate, na_rm = TRUE, ...) { ccc( data = data, truth = !! rlang::enquo(truth), estimate = !! rlang::enquo(estimate), # set bias = TRUE bias = TRUE, na_rm = na_rm, ... ) } # Use `new_numeric_metric()` to formalize this new metric function ccc_with_bias <- new_numeric_metric(ccc_with_bias, "maximize") multi_metric2 <- metric_set(rmse, rsq, ccc_with_bias) multi_metric2(solubility_test, truth = solubility, estimate = prediction)#> # A tibble: 3 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 rmse standard 0.722 #> 2 rsq standard 0.879 #> 3 ccc standard 0.937# --------------------------------------------------------------------------- # A class probability example: # Note that, when given class or class prob functions, # metric_set() returns a function with signature: # fn(data, truth, ..., estimate) # to be able to mix class and class prob metrics. # You must provide the `estimate` column by explicitly naming # the argument class_and_probs_metrics <- metric_set(roc_auc, pr_auc, accuracy) hpc_cv %>% group_by(Resample) %>% class_and_probs_metrics(obs, VF:L, estimate = pred)#> # A tibble: 30 x 4 #> Resample .metric .estimator .estimate #> <chr> <chr> <chr> <dbl> #> 1 Fold01 accuracy multiclass 0.726 #> 2 Fold02 accuracy multiclass 0.712 #> 3 Fold03 accuracy multiclass 0.758 #> 4 Fold04 accuracy multiclass 0.712 #> 5 Fold05 accuracy multiclass 0.712 #> 6 Fold06 accuracy multiclass 0.697 #> 7 Fold07 accuracy multiclass 0.675 #> 8 Fold08 accuracy multiclass 0.721 #> 9 Fold09 accuracy multiclass 0.673 #> 10 Fold10 accuracy multiclass 0.699 #> # … with 20 more rows