Nothing
test_that("two quantitative predictor marginal plot", {
svm_results <- readRDS(test_path("data", "svm_results.rds"))
p <- autoplot(svm_results)
expect_s3_class(p, "ggplot")
expect_equal(names(p$data), c("mean", "# resamples", ".metric", "name", "value"))
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "")
p <- autoplot(svm_results, metric = "accuracy")
expect_true(isTRUE(all.equal(unique(p$data$.metric), "accuracy")))
})
test_that("two quantitative predictor and one qualitative marginal plot", {
knn_results <- readRDS(test_path("data", "knn_results.rds"))
p <- autoplot(knn_results)
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c("Distance Weighting Function", "mean", "# resamples", ".metric", "name", "value")
)
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "")
expect_equal(p$labels$colour, "Distance Weighting Function")
})
test_that("not marginal plot with grid search", {
knn_results <- readRDS(test_path("data", "knn_results.rds"))
expect_snapshot(error = TRUE, autoplot(knn_results, type = "performance"))
expect_snapshot(error = TRUE, autoplot(knn_results, type = "parameters"))
})
test_that("marginal plot labels and transformations - irregular grid", {
svm_results <- readRDS(test_path("data", "svm_results.rds"))
p <- autoplot(svm_results)
expect_s3_class(p, "ggplot")
expect_equal(names(p$data), c("mean", "# resamples", ".metric", "name", "value"))
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "")
expect_equal(
sort(unique(p$data$name)),
c("%^*#", "Cost (log-2)", "Scale Factor (log-10)")
)
expect_equal(
sort(unique(collect_metrics(svm_results)$cost)),
sort(unique(2^(p$data$value[p$data$name == "Cost (log-2)"]))),
tolerance = 0.001
)
expect_equal(
sort(unique(collect_metrics(svm_results)$scale_factor)),
sort(unique(10^(p$data$value[p$data$name == "Scale Factor (log-10)"]))),
tolerance = 0.001
)
p <- autoplot(svm_results, metric = "accuracy")
expect_true(isTRUE(all.equal(unique(p$data$.metric), "accuracy")))
})
# ------------------------------------------------------------------------------
test_that("marginal plot for iterative search", {
load(test_path("data", "test_objects.RData"))
p <- autoplot(mt_spln_knn_bo_sep)
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c("Distance Weighting Function", "mean", "# resamples", ".metric", "name", "value")
)
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "")
expect_equal(p$labels$colour, "Distance Weighting Function")
p <- autoplot(mt_spln_knn_bo_sep, metric = "rmse")
expect_true(isTRUE(all.equal(unique(p$data$.metric), "rmse")))
})
test_that("performance plot for iterative search", {
load(test_path("data", "test_objects.RData"))
p <- autoplot(mt_spln_knn_bo_sep, type = "performance")
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c(
"K", "weight_func", "deg_free", ".metric",
".estimator", "mean", "n", "std_err", ".config", ".iter"
)
)
expect_equal(rlang::get_expr(p$mapping$x), expr(.iter))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(p$labels$x, "Iteration")
expect_equal(p$labels$y, "mean")
expect_equal(p$labels$ymin, "mean - const * std_err")
expect_equal(p$labels$ymax, "mean + const * std_err")
p <- autoplot(mt_spln_knn_bo_sep, type = "performance", metric = "rmse")
expect_true(isTRUE(all.equal(unique(p$data$.metric), "rmse")))
p <- autoplot(mt_spln_knn_bo_sep, type = "performance", width = 0)
expect_true(isTRUE(all.equal(names(p$mapping), c("x", "y"))))
})
test_that("parameter plot for iterative search", {
load(test_path("data", "test_objects.RData"))
p <- autoplot(mt_spln_knn_bo_sep, type = "parameters")
expect_s3_class(p, "ggplot")
expect_equal(names(p$data), c(".iter", "name", "value"))
name_vals <- sort(unique(p$data$name))
expect_equal(name_vals, c("K", "Piecewise Polynomial Degree"))
expect_equal(rlang::get_expr(p$mapping$x), expr(.iter))
expect_equal(rlang::get_expr(p$mapping$y), expr(value))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "Iteration")
})
test_that("regular grid plot", {
rcv_results <- readRDS(test_path("data", "rcv_results.rds"))
svm_reg_results <- readRDS(test_path("data", "svm_reg_results.rds"))
p <- autoplot(rcv_results)
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c("degree", "wt df", "wt degree", "mean", "# resamples", ".metric", "name", "value")
)
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(rlang::get_expr(p$mapping$col), sym("wt df"))
expect_equal(rlang::get_expr(p$mapping$group), sym("wt df"))
expect_equal(p$facet$vars(), c(".metric", "degree", "wt degree"))
expect_equal(p$labels$y, "")
expect_equal(p$labels$x, "deg_free")
p <- autoplot(rcv_results, metric = "rmse")
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c("degree", "wt df", "wt degree", "mean", "# resamples", ".metric", "name", "value")
)
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(rlang::get_expr(p$mapping$col), sym("wt df"))
expect_equal(rlang::get_expr(p$mapping$group), sym("wt df"))
expect_equal(p$facet$vars(), c("degree", "wt degree"))
expect_equal(p$labels$y, "rmse")
expect_equal(p$labels$x, "deg_free")
p <- autoplot(svm_reg_results)
expect_s3_class(p, "ggplot")
expect_equal(
names(p$data),
c("%^*#", "Scale Factor", "mean", "# resamples", ".metric", "name", "value")
)
expect_equal(rlang::get_expr(p$mapping$x), expr(value))
expect_equal(rlang::get_expr(p$mapping$y), expr(mean))
expect_equal(rlang::get_expr(p$mapping$col), sym("%^*#"))
expect_equal(rlang::get_expr(p$mapping$group), sym("%^*#"))
expect_equal(p$facet$vars(), c(".metric", "Scale Factor"))
expect_equal(p$labels$y, "")
expect_equal(p$labels$colour, as.name("%^*#"))
expect_equal(p$labels$x, "Cost")
expect_equal(p$labels$group, "%^*#")
expect_equal(class(p$scales$scales[[1]]$trans), "trans")
expect_equal(p$scales$scales[[1]]$trans$name, "log-2")
expect_equal(unique(p$data$name), "Cost")
})
test_that("coord_obs_pred", {
data(solubility_test, package = "modeldata")
library(ggplot2)
p <-
ggplot(solubility_test, aes(x = solubility, y = prediction)) +
geom_abline(lty = 2) +
geom_point(alpha = 0.5)
rng <- range(solubility_test[[1]], solubility_test[[2]])
p2 <- p + coord_obs_pred()
expect_error(print(p2), regexp = NA)
expect_true(inherits(p2$coordinates, "CoordObsPred"))
expect_equal(p2$coordinates$limits$x, rng)
expect_equal(p2$coordinates$limits$y, rng)
expect_equal(p2$coordinates$ratio, 1)
solubility_test$solubility[1] <- NA
p3 <-
ggplot(solubility_test, aes(x = solubility, y = prediction)) +
geom_abline(lty = 2) +
geom_point(alpha = 0.5)
expect_snapshot_warning(print(p3 + coord_obs_pred()))
})
test_that("1D regular grid x labels", {
set.seed(1)
res <-
parsnip::svm_rbf(cost = tune()) %>%
parsnip::set_engine("kernlab") %>%
parsnip::set_mode("regression") %>%
tune_grid(mpg ~ ., resamples = rsample::vfold_cv(mtcars, v = 5), grid = 3)
expect_equal(autoplot(res)$labels$x, c(cost = "Cost"))
})
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