For objects produced by the tune_*() functions, there may only be a subset
of tuning parameter combinations of interest. For large data sets, it might be
helpful to be able to remove some results. This function trims the .metrics
column of unwanted results as well as columns .predictions and .extracts
(if they were requested).
Arguments
- x
An object of class
tune_resultsthat has multiple tuning parameters.- ...
Expressions that return a logical value, and are defined in terms of the tuning parameter values. If multiple expressions are included, they are combined with the
&operator. Only rows for which all conditions evaluate toTRUEare kept.- parameters
A tibble of tuning parameter values that can be used to filter the predicted values before processing. This tibble should only have columns for tuning parameter identifiers (e.g.
"my_param"iftune("my_param")was used). There can be multiple rows and one or more columns. If used, this parameter must be named.
Value
A version of x where the lists columns only retain the parameter
combinations in parameters or satisfied by the filtering logic.
Details
Removing some parameter combinations might affect the results of autoplot()
for the object.
Examples
library(dplyr)
library(tibble)
# For grid search:
data("example_ames_knn")
## -----------------------------------------------------------------------------
# select all combinations using the 'rank' weighting scheme
ames_grid_search %>%
collect_metrics()
#> # A tibble: 20 × 11
#> K weight_func dist_power lon lat .metric .estimator mean
#> <int> <chr> <dbl> <int> <int> <chr> <chr> <dbl>
#> 1 35 optimal 1.32 8 1 rmse standard 0.0785
#> 2 35 optimal 1.32 8 1 rsq standard 0.823
#> 3 35 rank 1.29 3 13 rmse standard 0.0809
#> 4 35 rank 1.29 3 13 rsq standard 0.814
#> 5 21 cos 0.626 1 4 rmse standard 0.0746
#> 6 21 cos 0.626 1 4 rsq standard 0.836
#> 7 4 biweight 0.311 8 4 rmse standard 0.0777
#> 8 4 biweight 0.311 8 4 rsq standard 0.814
#> 9 32 triangular 0.165 9 15 rmse standard 0.0770
#> 10 32 triangular 0.165 9 15 rsq standard 0.826
#> 11 3 rank 1.86 10 15 rmse standard 0.0875
#> 12 3 rank 1.86 10 15 rsq standard 0.762
#> 13 40 triangular 0.167 11 7 rmse standard 0.0778
#> 14 40 triangular 0.167 11 7 rsq standard 0.822
#> 15 12 epanechnikov 1.53 4 7 rmse standard 0.0774
#> 16 12 epanechnikov 1.53 4 7 rsq standard 0.820
#> 17 5 rank 0.411 2 7 rmse standard 0.0740
#> 18 5 rank 0.411 2 7 rsq standard 0.833
#> 19 33 triweight 0.511 10 3 rmse standard 0.0728
#> 20 33 triweight 0.511 10 3 rsq standard 0.842
#> # ℹ 3 more variables: n <int>, std_err <dbl>, .config <chr>
filter_parameters(ames_grid_search, weight_func == "rank") %>%
collect_metrics()
#> # A tibble: 6 × 11
#> K weight_func dist_power lon lat .metric .estimator mean n
#> <int> <chr> <dbl> <int> <int> <chr> <chr> <dbl> <int>
#> 1 35 rank 1.29 3 13 rmse standard 0.0809 10
#> 2 35 rank 1.29 3 13 rsq standard 0.814 10
#> 3 3 rank 1.86 10 15 rmse standard 0.0875 10
#> 4 3 rank 1.86 10 15 rsq standard 0.762 10
#> 5 5 rank 0.411 2 7 rmse standard 0.0740 10
#> 6 5 rank 0.411 2 7 rsq standard 0.833 10
#> # ℹ 2 more variables: std_err <dbl>, .config <chr>
rank_only <- tibble::tibble(weight_func = "rank")
filter_parameters(ames_grid_search, parameters = rank_only) %>%
collect_metrics()
#> # A tibble: 6 × 11
#> K weight_func dist_power lon lat .metric .estimator mean n
#> <int> <chr> <dbl> <int> <int> <chr> <chr> <dbl> <int>
#> 1 35 rank 1.29 3 13 rmse standard 0.0809 10
#> 2 35 rank 1.29 3 13 rsq standard 0.814 10
#> 3 3 rank 1.86 10 15 rmse standard 0.0875 10
#> 4 3 rank 1.86 10 15 rsq standard 0.762 10
#> 5 5 rank 0.411 2 7 rmse standard 0.0740 10
#> 6 5 rank 0.411 2 7 rsq standard 0.833 10
#> # ℹ 2 more variables: std_err <dbl>, .config <chr>
## -----------------------------------------------------------------------------
# Keep only the results from the numerically best combination
ames_iter_search %>%
collect_metrics()
#> # A tibble: 40 × 12
#> K weight_func dist_power lon lat .metric .estimator mean
#> <int> <chr> <dbl> <int> <int> <chr> <chr> <dbl>
#> 1 35 optimal 1.32 8 1 rmse standard 0.0785
#> 2 35 optimal 1.32 8 1 rsq standard 0.823
#> 3 35 rank 1.29 3 13 rmse standard 0.0809
#> 4 35 rank 1.29 3 13 rsq standard 0.814
#> 5 21 cos 0.626 1 4 rmse standard 0.0746
#> 6 21 cos 0.626 1 4 rsq standard 0.836
#> 7 4 biweight 0.311 8 4 rmse standard 0.0777
#> 8 4 biweight 0.311 8 4 rsq standard 0.814
#> 9 32 triangular 0.165 9 15 rmse standard 0.0770
#> 10 32 triangular 0.165 9 15 rsq standard 0.826
#> # ℹ 30 more rows
#> # ℹ 4 more variables: n <int>, std_err <dbl>, .config <chr>, .iter <int>
best_param <- select_best(ames_iter_search, metric = "rmse")
ames_iter_search %>%
filter_parameters(parameters = best_param) %>%
collect_metrics()
#> # A tibble: 2 × 12
#> K weight_func dist_power lon lat .metric .estimator mean n
#> <int> <chr> <dbl> <int> <int> <chr> <chr> <dbl> <int>
#> 1 33 triweight 0.511 10 3 rmse standard 0.0728 10
#> 2 33 triweight 0.511 10 3 rsq standard 0.842 10
#> # ℹ 3 more variables: std_err <dbl>, .config <chr>, .iter <int>