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simonpcouchsimonpcouch
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update snapshots with space-filling grids (#218)
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‎tests/testthat/_snaps/survival-tune-show-best.md‎

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# A tibble: 5 x 9
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cost_complexity .metric .estimator .eval_time mean n std_err .config
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<dbl> <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
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1 1.26e- 9 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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2 1.31e- 6 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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3 3.55e- 8 brier_surv~ standard 100 0.0114 10 0.00312 Iter2
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4 1.00e-10 brier_surv~ standard 100 0.0114 10 0.00312 Iter3
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5 3.91e- 5 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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1 7.81e- 5 brier_surv~ standard 100 0.0110 10 0.00316 Prepro~
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2 1.03e-10 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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3 9.12e- 8 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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4 3.56e- 6 brier_surv~ standard 100 0.0114 10 0.00312 Iter1
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5 2.24e- 9 brier_surv~ standard 100 0.0114 10 0.00312 Iter2
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# i 1 more variable: .iter <int>
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---
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# A tibble: 5 x 8
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cost_complexity .metric .estimator .eval_time mean n std_err .config
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<dbl> <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
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1 0.0706 concordance~ standard NA 0.297 10 0.0118 Prepro~
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2 0.0000128 concordance~ standard NA 0.278 10 0.0147 Prepro~
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3 0.0000591 concordance~ standard NA 0.275 10 0.0152 Prepro~
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4 0.0000959 concordance~ standard NA 0.274 10 0.0149 Prepro~
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5 0.000374 concordance~ standard NA 0.256 10 0.0133 Prepro~
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1 0.0867 concordance~ standard NA 0.297 10 0.0118 Prepro~
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2 0.0000192 concordance~ standard NA 0.279 10 0.0147 Prepro~
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3 0.0000107 concordance~ standard NA 0.278 10 0.0147 Prepro~
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4 0.0000384 concordance~ standard NA 0.277 10 0.0156 Prepro~
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5 0.000118 concordance~ standard NA 0.270 10 0.0141 Prepro~
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---
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# A tibble: 5 x 8
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cost_complexity .metric .estimator .eval_time mean n std_err .config
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<dbl> <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
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1 0.0706 concordance~ standard NA 0.297 10 0.0118 Prepro~
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2 0.0000128 concordance~ standard NA 0.278 10 0.0147 Prepro~
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3 0.0000591 concordance~ standard NA 0.275 10 0.0152 Prepro~
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4 0.0000959 concordance~ standard NA 0.274 10 0.0149 Prepro~
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5 0.000374 concordance~ standard NA 0.256 10 0.0133 Prepro~
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1 0.0867 concordance~ standard NA 0.297 10 0.0118 Prepro~
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2 0.0000192 concordance~ standard NA 0.279 10 0.0147 Prepro~
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3 0.0000107 concordance~ standard NA 0.278 10 0.0147 Prepro~
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4 0.0000384 concordance~ standard NA 0.277 10 0.0156 Prepro~
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5 0.000118 concordance~ standard NA 0.270 10 0.0141 Prepro~
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# A tibble: 5 x 8
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cost_complexity .metric .estimator .eval_time mean n std_err .config
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<dbl> <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
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1 0.0000591 brier_surv~ standard 100 0.0110 10 0.00314 Prepro~
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2 0.0000959 brier_surv~ standard 100 0.0110 10 0.00317 Prepro~
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3 0.0000128 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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4 0.000374 brier_surv~ standard 100 0.0114 10 0.00340 Prepro~
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5 0.000822 brier_surv~ standard 100 0.0124 10 0.00319 Prepro~
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1 0.000327 brier_surv~ standard 100 0.0108 10 0.00329 Prepro~
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2 0.000118 brier_surv~ standard 100 0.0110 10 0.00317 Prepro~
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3 0.0000192 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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4 0.0000384 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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5 0.0000107 brier_surv~ standard 100 0.0114 10 0.00312 Prepro~
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---
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# A tibble: 5 x 8
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cost_complexity .metric .estimator .eval_time mean n std_err .config
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<dbl> <chr> <chr> <dbl> <dbl> <int> <dbl> <chr>
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1 0.0000128 concordance~ standard NA 0.278 10 0.0147 Prepro~
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2 0.0000591 concordance~ standard NA 0.275 10 0.0152 Prepro~
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3 0.0000959 concordance~ standard NA 0.274 10 0.0149 Prepro~
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4 0.000374 concordance~ standard NA 0.256 10 0.0133 Prepro~
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5 0.000822 concordance~ standard NA 0.238 10 0.0182 Prepro~
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1 0.0000192 concordance~ standard NA 0.279 10 0.0147 Prepro~
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2 0.0000107 concordance~ standard NA 0.278 10 0.0147 Prepro~
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3 0.0000384 concordance~ standard NA 0.277 10 0.0156 Prepro~
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4 0.000118 concordance~ standard NA 0.270 10 0.0141 Prepro~
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5 0.000327 concordance~ standard NA 0.259 10 0.0141 Prepro~
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‎tests/testthat/test-engine-parameters-ranger.R‎

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@@ -15,6 +15,9 @@ rs <- bootstraps(mtcars, times = 5)
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## -----------------------------------------------------------------------------
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test_that('grid search', {
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# pending tidymodels/dials#347
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skip_if_not_installed("dials", minimum_version = "1.3.0.9001")
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set.seed(2893)
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expect_error(
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rf_tune <-
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## -----------------------------------------------------------------------------
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test_that('Bayes search', {
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# pending tidymodels/dials#347
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skip_if_not_installed("dials", minimum_version = "1.3.0.9001")
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set.seed(2893)
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expect_error(
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rf_search <-

‎tests/testthat/test-survival-tune-show-best.R‎

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@@ -2,7 +2,7 @@
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test_that("show_best with censored data - integrated metric - grid", {
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skip_if_not_installed("parsnip", minimum_version = "1.1.1.9007")
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skip_if_not_installed("tune", minimum_version = "1.1.2.9012")
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skip_if_not_installed("tune", minimum_version = "1.2.1.9000")
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obj <- make_churn_cens_objects()
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@@ -43,7 +43,7 @@ test_that("show_best with censored data - integrated metric - grid", {
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test_that("show_best with censored data - dynamic metric - bayes", {
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skip_if_not_installed("parsnip", minimum_version = "1.1.1.9007")
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skip_if_not_installed("tune", minimum_version = "1.1.2.9012")
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skip_if_not_installed("tune", minimum_version = "1.2.1.9000")
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obj <- make_churn_cens_objects()
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test_that("show_best with censored data - static metric - anova racing", {
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skip_if_not_installed("parsnip", minimum_version = "1.1.1.9007")
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skip_if_not_installed("tune", minimum_version = "1.1.2.9020")
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skip_if_not_installed("tune", minimum_version = "1.2.1.9000")
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skip_if_not_installed("finetune", minimum_version = "1.1.0.9004")
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obj <- make_churn_cens_objects()
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test_that("show_best with censored data - static metric (+dyn) - W/L racing", {
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skip_if_not_installed("parsnip", minimum_version = "1.1.1.9007")
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skip_if_not_installed("tune", minimum_version = "1.1.2.9020")
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skip_if_not_installed("tune", minimum_version = "1.2.1.9000")
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skip_if_not_installed("finetune", minimum_version = "1.1.0.9004")
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obj <- make_churn_cens_objects()
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test_that("show_best with censored data - dyn metric (+stc) - W/L racing", {
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skip_if_not_installed("parsnip", minimum_version = "1.1.1.9007")
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skip_if_not_installed("tune", minimum_version = "1.1.2.9012")
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skip_if_not_installed("tune", minimum_version = "1.2.1.9000")
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skip_if_not_installed("finetune", minimum_version = "1.1.0.9004")
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obj <- make_churn_cens_objects()

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