tests/testthat/test_evaluate_residuals.R
In LudvigOlsen/cvms: Cross-Validation for Model Selection
library(cvms)
context("evaluate_residuals()")
test_that("evaluate_residuals() have expected output", {
xpectr::set_test_seed(2)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
xpectr::set_test_seed(2)
# xpectr::gxs_function(fn = evaluate_residuals,
# args_values = list(
# data = list(df, dplyr::group_by(df, .data$grp), NA, 2, tibble::tibble()),
# prediction_col = list("p", "t", "k", NA, 2),
# target_col = list("t", "p", "k", NA, 2),
# metrics = list(list("all" = TRUE), 4, NA)
# ), indentation = 2)
## Testing 'evaluate_residuals' ####
## Initially generated by xpectr
# Testing different combinations of argument values
# Testing evaluate_residuals(data = df, predictions_co...
xpectr::set_test_seed(42)
# Assigning output
output_11848 <- evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_11848),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11848[["RMSE"]],
0.35497,
tolerance = 1e-4)
expect_equal(
output_11848[["MAE"]],
0.28557,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(RNG)"]],
0.39379,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(IQR)"]],
0.63032,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(STD)"]],
1.16632,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(AVG)"]],
0.69532,
tolerance = 1e-4)
expect_equal(
output_11848[["RSE"]],
1.43189,
tolerance = 1e-4)
expect_equal(
output_11848[["RRSE"]],
1.19661,
tolerance = 1e-4)
expect_equal(
output_11848[["RAE"]],
1.10497,
tolerance = 1e-4)
expect_equal(
output_11848[["RMSLE"]],
0.23603,
tolerance = 1e-4)
expect_equal(
output_11848[["MALE"]],
0.18896,
tolerance = 1e-4)
expect_equal(
output_11848[["MAPE"]],
0.74249,
tolerance = 1e-4)
expect_equal(
output_11848[["MSE"]],
0.126,
tolerance = 1e-4)
expect_equal(
output_11848[["TAE"]],
5.71144,
tolerance = 1e-4)
expect_equal(
output_11848[["TSE"]],
2.52009,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_11848),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11848),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11848),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11848),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11848)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = dplyr::group_by(df...
# Changed from baseline: data = dplyr::group_b...
xpectr::set_test_seed(42)
# Assigning output
output_17023 <- evaluate_residuals(data = dplyr::group_by(df, .data$grp), prediction_col = "p", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_17023),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_17023[["grp"]],
c(1, 2, 3, 4, 5),
tolerance = 1e-4)
expect_equal(
output_17023[["RMSE"]],
c(0.44147, 0.17496, 0.27494, 0.43813, 0.37009),
tolerance = 1e-4)
expect_equal(
output_17023[["MAE"]],
c(0.42508, 0.13528, 0.21217, 0.35566, 0.29967),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(RNG)"]],
c(0.58196, 0.20651, 0.45248, 1.4606, 0.42595),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(IQR)"]],
c(1.063, 0.31276, 0.94061, 1.60497, 1.13615),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(STD)"]],
c(1.28827, 0.4406, 1.01305, 2.6853, 1.02785),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(AVG)"]],
c(0.69671, 0.34192, 0.45955, 1.38902, 0.7499),
tolerance = 1e-4)
expect_equal(
output_17023[["RSE"]],
c(2.21285, 0.25884, 1.36835, 9.61444, 1.40864),
tolerance = 1e-4)
expect_equal(
output_17023[["RRSE"]],
c(1.48757, 0.50876, 1.16977, 3.10072, 1.18686),
tolerance = 1e-4)
expect_equal(
output_17023[["RAE"]],
c(1.60489, 0.41285, 1.06777, 2.5226, 1.18263),
tolerance = 1e-4)
expect_equal(
output_17023[["RMSLE"]],
c(0.31106, 0.11261, 0.17139, 0.281, 0.24654),
tolerance = 1e-4)
expect_equal(
output_17023[["MALE"]],
c(0.29118, 0.08826, 0.13266, 0.22696, 0.20577),
tolerance = 1e-4)
expect_equal(
output_17023[["MAPE"]],
c(1.0771, 0.26243, 0.33952, 1.36661, 0.66679),
tolerance = 1e-4)
expect_equal(
output_17023[["MSE"]],
c(0.19489, 0.03061, 0.07559, 0.19196, 0.13697),
tolerance = 1e-4)
expect_equal(
output_17023[["TAE"]],
c(1.70033, 0.54113, 0.84867, 1.42263, 1.19867),
tolerance = 1e-4)
expect_equal(
output_17023[["TSE"]],
c(0.77958, 0.12245, 0.30236, 0.76783, 0.54787),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_17023),
c("grp", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_17023),
c("integer", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_17023),
c("integer", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_17023),
c(5L, 16L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_17023)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = NA, predictions_co...
# Changed from baseline: data = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15733 <- xpectr::capture_side_effects(evaluate_residuals(data = NA, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15733[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'logical'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15733[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = 2, prediction_col...
# Changed from baseline: data = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11680 <- xpectr::capture_side_effects(evaluate_residuals(data = 2, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11680[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11680[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = tibble::tibble(), ...
# Changed from baseline: data = tibble::tibble()
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19438 <- xpectr::capture_side_effects(evaluate_residuals(data = tibble::tibble(), prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19438[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must have at least 1 rows, but has 0 rows."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19438[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = NULL, predictions_...
# Changed from baseline: data = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19434 <- xpectr::capture_side_effects(evaluate_residuals(data = NULL, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19434[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19434[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = "t"
xpectr::set_test_seed(42)
# Assigning output
output_11291 <- evaluate_residuals(data = df, prediction_col = "t", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_11291),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11291[["RMSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(RNG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(IQR)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(STD)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(AVG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RRSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RMSLE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MALE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MAPE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["TAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["TSE"]],
0,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_11291),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11291),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11291),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11291),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11291)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = "k"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18334 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "k", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_18334[['error']], lowercase = TRUE),
xpectr::strip("must include the elements {k,t}.", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18334[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14680 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = NA, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14680[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14680[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15499 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = 2, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15499[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': Must be of type 'string', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15499[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15526 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = NULL, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15526[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': Must be of type 'string', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15526[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = "k"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12388 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "k", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_12388[['error']], lowercase = TRUE),
xpectr::strip("must include the elements {p,k}.", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12388[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17605 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = NA, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17605[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17605[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11808 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = 2, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11808[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': Must be of type 'string', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11808[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = "p"
xpectr::set_test_seed(42)
# Assigning output
output_14052 <- evaluate_residuals(data = df, prediction_col = "p", target_col = "p", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_14052),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_14052[["RMSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(RNG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(IQR)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(STD)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(AVG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RRSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RMSLE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MALE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MAPE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["TAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["TSE"]],
0,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_14052),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_14052),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_14052),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_14052),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_14052)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18535 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = NULL, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_18535[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': Must be of type 'string', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18535[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = 4
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19763 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = 4), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19763[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19763[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12258 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = NA), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_12258[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'logical'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12258[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14448 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = NULL), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14448[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14448[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'evaluate_residuals' ####
#
})
test_that("evaluate_residuals() have expected output", {
xpectr::set_test_seed(1)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
ungrouped_res <- evaluate_residuals(
data = df,
target_col = "t",
prediction_col = "p",
metrics = "all"
)
expect_equal(
colnames(ungrouped_res),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE")
)
expect_equal(ungrouped_res$RMSE, 0.4383415, tolerance = 1e-5)
expect_equal(ungrouped_res$MAE, 0.3493256, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(RNG)`, 0.4712743, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(IQR)`, 1.028314, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(STD)`, 1.532031, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(AVG)`, 0.789566, tolerance = 1e-5)
expect_equal(ungrouped_res$RMSLE, 0.2955423, tolerance = 1e-5)
expect_equal(ungrouped_res$MALE, 0.2355741, tolerance = 1e-5)
expect_equal(ungrouped_res$RAE, 1.425217, tolerance = 1e-5)
expect_equal(ungrouped_res$RSE, 2.470652, tolerance = 1e-5)
expect_equal(ungrouped_res$RRSE, 1.571831, tolerance = 1e-5)
expect_equal(ungrouped_res$MAPE, 0.9233602, tolerance = 1e-5)
expect_equal(ungrouped_res$MSE, 0.1921433, tolerance = 1e-5)
expect_equal(ungrouped_res$TAE, 6.986512, tolerance = 1e-5)
expect_equal(ungrouped_res$TSE, 3.842867, tolerance = 1e-5)
grouped_res <- evaluate_residuals(
df %>% dplyr::group_by(grp),
target_col = "t",
prediction_col = "p",
metrics = "all"
)
expect_equal(
colnames(grouped_res),
c("grp", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE")
)
expect_equal(grouped_res$RMSE,
c(
0.451565647616432, 0.519060010936869, 0.475013011667194, 0.454888553611741,
0.234138573611605
),
tolerance = 1e-5
)
expect_equal(grouped_res$MAE,
c(
0.407086299266666, 0.397724184789695, 0.358668561675586, 0.391197844815906,
0.191951038548723
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(RNG)`,
c(
0.652160302469174, 0.675755108193839, 1.1335399485195, 0.861249796972752,
0.327165018103644
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(IQR)`,
c(
1.30041734700364, 1.15052496357359, 3.81745140187305, 1.44042831439479,
0.386983932227589
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(STD)`,
c(
1.43814486544896, 1.50905226589782, 2.60289783098973, 1.81516488970619,
0.624379332243931
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(AVG)`,
c(0.967171509741637, 0.93906108387384, 0.652360444408903, 0.79060841208794,
0.517217611295255),
tolerance = 1e-5
)
expect_equal(grouped_res$RMSLE,
c(
0.2886413430748, 0.366211402088592, 0.314237354222149, 0.304503365190871,
0.166835911010467
),
tolerance = 1e-5
)
expect_equal(grouped_res$MALE,
c(
0.263279042673231, 0.281636667412819, 0.235196273801541, 0.260569609060969,
0.137188700334378
),
tolerance = 1e-5
)
expect_equal(grouped_res$RAE,
c(
1.76112392170928, 1.42859014846979, 2.71964745212324, 2.02383072091832,
0.598062880532025
),
tolerance = 1e-5
)
expect_equal(grouped_res$RSE,
c(
2.75768087202294, 3.03631832161513, 9.03343615809475, 4.39309810242944,
0.51979940071117
),
tolerance = 1e-5
)
expect_equal(grouped_res$RRSE,
c(
1.66062665040127, 1.74250346387464, 3.00556752679003, 2.09597187539085,
0.720971151094945
),
tolerance = 1e-5
)
expect_equal(grouped_res$MAPE,
c(
1.19356123357169, 0.979598260014201, 0.432928785595697, 0.665919069119316,
1.34479362791513
),
tolerance = 1e-5
)
expect_equal(grouped_res$MSE,
c(
0.203911534107247, 0.269423294953783, 0.225637361253137, 0.206923596206982,
0.0548208716528769
),
tolerance = 1e-5
)
expect_equal(grouped_res$TAE,
c(
1.62834519706666, 1.59089673915878, 1.43467424670234, 1.56479137926362,
0.767804154194891
),
tolerance = 1e-5
)
expect_equal(grouped_res$TSE,
c(
0.815646136428989, 1.07769317981513, 0.90254944501255, 0.827694384827926,
0.219283486611508
),
tolerance = 1e-5
)
})
test_that("call_evaluate_residuals() creates NA results correcly", {
xpectr::set_test_seed(1)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
na_tibble <- tibble::tibble(
"grp" = 1:5,
"RMSE" = NA,
"MAE" = NA,
"TSE" = NA,
"NRMSE(RNG)" = NA,
"NRMSE(IQR)" = NA
)
expect_equal(
call_evaluate_residuals(
data = df, target_col = "t",
prediction_col = "p",
metrics = c("RMSE", "MAE", "TSE", "NRMSE(RNG)", "NRMSE(IQR)"),
return_nas = TRUE
),
na_tibble[1, 2:6]
)
expect_equal(
call_evaluate_residuals(
data = df %>% dplyr::group_by(grp),
target_col = "t",
prediction_col = "p",
metrics = c("RMSE", "MAE", "TSE", "NRMSE(RNG)", "NRMSE(IQR)"),
return_nas = TRUE
),
na_tibble
)
})
LudvigOlsen/cvms documentation built on March 2, 2024, 1:54 p.m.
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library(cvms)
context("evaluate_residuals()")
test_that("evaluate_residuals() have expected output", {
xpectr::set_test_seed(2)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
xpectr::set_test_seed(2)
# xpectr::gxs_function(fn = evaluate_residuals,
# args_values = list(
# data = list(df, dplyr::group_by(df, .data$grp), NA, 2, tibble::tibble()),
# prediction_col = list("p", "t", "k", NA, 2),
# target_col = list("t", "p", "k", NA, 2),
# metrics = list(list("all" = TRUE), 4, NA)
# ), indentation = 2)
## Testing 'evaluate_residuals' ####
## Initially generated by xpectr
# Testing different combinations of argument values
# Testing evaluate_residuals(data = df, predictions_co...
xpectr::set_test_seed(42)
# Assigning output
output_11848 <- evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_11848),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11848[["RMSE"]],
0.35497,
tolerance = 1e-4)
expect_equal(
output_11848[["MAE"]],
0.28557,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(RNG)"]],
0.39379,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(IQR)"]],
0.63032,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(STD)"]],
1.16632,
tolerance = 1e-4)
expect_equal(
output_11848[["NRMSE(AVG)"]],
0.69532,
tolerance = 1e-4)
expect_equal(
output_11848[["RSE"]],
1.43189,
tolerance = 1e-4)
expect_equal(
output_11848[["RRSE"]],
1.19661,
tolerance = 1e-4)
expect_equal(
output_11848[["RAE"]],
1.10497,
tolerance = 1e-4)
expect_equal(
output_11848[["RMSLE"]],
0.23603,
tolerance = 1e-4)
expect_equal(
output_11848[["MALE"]],
0.18896,
tolerance = 1e-4)
expect_equal(
output_11848[["MAPE"]],
0.74249,
tolerance = 1e-4)
expect_equal(
output_11848[["MSE"]],
0.126,
tolerance = 1e-4)
expect_equal(
output_11848[["TAE"]],
5.71144,
tolerance = 1e-4)
expect_equal(
output_11848[["TSE"]],
2.52009,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_11848),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11848),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11848),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11848),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11848)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = dplyr::group_by(df...
# Changed from baseline: data = dplyr::group_b...
xpectr::set_test_seed(42)
# Assigning output
output_17023 <- evaluate_residuals(data = dplyr::group_by(df, .data$grp), prediction_col = "p", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_17023),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_17023[["grp"]],
c(1, 2, 3, 4, 5),
tolerance = 1e-4)
expect_equal(
output_17023[["RMSE"]],
c(0.44147, 0.17496, 0.27494, 0.43813, 0.37009),
tolerance = 1e-4)
expect_equal(
output_17023[["MAE"]],
c(0.42508, 0.13528, 0.21217, 0.35566, 0.29967),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(RNG)"]],
c(0.58196, 0.20651, 0.45248, 1.4606, 0.42595),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(IQR)"]],
c(1.063, 0.31276, 0.94061, 1.60497, 1.13615),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(STD)"]],
c(1.28827, 0.4406, 1.01305, 2.6853, 1.02785),
tolerance = 1e-4)
expect_equal(
output_17023[["NRMSE(AVG)"]],
c(0.69671, 0.34192, 0.45955, 1.38902, 0.7499),
tolerance = 1e-4)
expect_equal(
output_17023[["RSE"]],
c(2.21285, 0.25884, 1.36835, 9.61444, 1.40864),
tolerance = 1e-4)
expect_equal(
output_17023[["RRSE"]],
c(1.48757, 0.50876, 1.16977, 3.10072, 1.18686),
tolerance = 1e-4)
expect_equal(
output_17023[["RAE"]],
c(1.60489, 0.41285, 1.06777, 2.5226, 1.18263),
tolerance = 1e-4)
expect_equal(
output_17023[["RMSLE"]],
c(0.31106, 0.11261, 0.17139, 0.281, 0.24654),
tolerance = 1e-4)
expect_equal(
output_17023[["MALE"]],
c(0.29118, 0.08826, 0.13266, 0.22696, 0.20577),
tolerance = 1e-4)
expect_equal(
output_17023[["MAPE"]],
c(1.0771, 0.26243, 0.33952, 1.36661, 0.66679),
tolerance = 1e-4)
expect_equal(
output_17023[["MSE"]],
c(0.19489, 0.03061, 0.07559, 0.19196, 0.13697),
tolerance = 1e-4)
expect_equal(
output_17023[["TAE"]],
c(1.70033, 0.54113, 0.84867, 1.42263, 1.19867),
tolerance = 1e-4)
expect_equal(
output_17023[["TSE"]],
c(0.77958, 0.12245, 0.30236, 0.76783, 0.54787),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_17023),
c("grp", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_17023),
c("integer", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_17023),
c("integer", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_17023),
c(5L, 16L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_17023)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = NA, predictions_co...
# Changed from baseline: data = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15733 <- xpectr::capture_side_effects(evaluate_residuals(data = NA, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15733[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'logical'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15733[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = 2, prediction_col...
# Changed from baseline: data = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11680 <- xpectr::capture_side_effects(evaluate_residuals(data = 2, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11680[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11680[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = tibble::tibble(), ...
# Changed from baseline: data = tibble::tibble()
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19438 <- xpectr::capture_side_effects(evaluate_residuals(data = tibble::tibble(), prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19438[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must have at least 1 rows, but has 0 rows."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19438[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = NULL, predictions_...
# Changed from baseline: data = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19434 <- xpectr::capture_side_effects(evaluate_residuals(data = NULL, prediction_col = "p", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19434[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'data': Must be of type 'data.frame', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19434[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = "t"
xpectr::set_test_seed(42)
# Assigning output
output_11291 <- evaluate_residuals(data = df, prediction_col = "t", target_col = "t", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_11291),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_11291[["RMSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(RNG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(IQR)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(STD)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["NRMSE(AVG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RRSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["RMSLE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MALE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MAPE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["MSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["TAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_11291[["TSE"]],
0,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_11291),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_11291),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_11291),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_11291),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_11291)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = "k"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18334 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "k", target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_18334[['error']], lowercase = TRUE),
xpectr::strip("must include the elements {k,t}.", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18334[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14680 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = NA, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14680[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14680[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15499 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = 2, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15499[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': Must be of type 'string', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15499[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: prediction_col = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15526 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = NULL, target_col = "t", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15526[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'prediction_col': Must be of type 'string', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15526[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = "k"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12388 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "k", metrics = list(all = TRUE)), reset_seed = TRUE)
expect_match(
xpectr::strip(side_effects_12388[['error']], lowercase = TRUE),
xpectr::strip("must include the elements {p,k}.", lowercase = TRUE),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12388[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17605 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = NA, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17605[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17605[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = 2
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11808 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = 2, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11808[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': Must be of type 'string', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11808[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = "p"
xpectr::set_test_seed(42)
# Assigning output
output_14052 <- evaluate_residuals(data = df, prediction_col = "p", target_col = "p", metrics = list(all = TRUE))
# Testing class
expect_equal(
class(output_14052),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_14052[["RMSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(RNG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(IQR)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(STD)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["NRMSE(AVG)"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RRSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["RMSLE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MALE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MAPE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["MSE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["TAE"]],
0,
tolerance = 1e-4)
expect_equal(
output_14052[["TSE"]],
0,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_14052),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_14052),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_14052),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_14052),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_14052)),
character(0),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: target_col = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18535 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = NULL, metrics = list(all = TRUE)), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_18535[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'target_col': Must be of type 'string', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18535[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = 4
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19763 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = 4), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19763[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'double'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19763[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12258 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = NA), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_12258[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'logical'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12258[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing evaluate_residuals(data = df, predictions_co...
# Changed from baseline: metrics = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14448 <- xpectr::capture_side_effects(evaluate_residuals(data = df, prediction_col = "p", target_col = "t", metrics = NULL), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14448[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'metrics': Must be of type 'list', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14448[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'evaluate_residuals' ####
#
})
test_that("evaluate_residuals() have expected output", {
xpectr::set_test_seed(1)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
ungrouped_res <- evaluate_residuals(
data = df,
target_col = "t",
prediction_col = "p",
metrics = "all"
)
expect_equal(
colnames(ungrouped_res),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE")
)
expect_equal(ungrouped_res$RMSE, 0.4383415, tolerance = 1e-5)
expect_equal(ungrouped_res$MAE, 0.3493256, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(RNG)`, 0.4712743, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(IQR)`, 1.028314, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(STD)`, 1.532031, tolerance = 1e-5)
expect_equal(ungrouped_res$`NRMSE(AVG)`, 0.789566, tolerance = 1e-5)
expect_equal(ungrouped_res$RMSLE, 0.2955423, tolerance = 1e-5)
expect_equal(ungrouped_res$MALE, 0.2355741, tolerance = 1e-5)
expect_equal(ungrouped_res$RAE, 1.425217, tolerance = 1e-5)
expect_equal(ungrouped_res$RSE, 2.470652, tolerance = 1e-5)
expect_equal(ungrouped_res$RRSE, 1.571831, tolerance = 1e-5)
expect_equal(ungrouped_res$MAPE, 0.9233602, tolerance = 1e-5)
expect_equal(ungrouped_res$MSE, 0.1921433, tolerance = 1e-5)
expect_equal(ungrouped_res$TAE, 6.986512, tolerance = 1e-5)
expect_equal(ungrouped_res$TSE, 3.842867, tolerance = 1e-5)
grouped_res <- evaluate_residuals(
df %>% dplyr::group_by(grp),
target_col = "t",
prediction_col = "p",
metrics = "all"
)
expect_equal(
colnames(grouped_res),
c("grp", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE")
)
expect_equal(grouped_res$RMSE,
c(
0.451565647616432, 0.519060010936869, 0.475013011667194, 0.454888553611741,
0.234138573611605
),
tolerance = 1e-5
)
expect_equal(grouped_res$MAE,
c(
0.407086299266666, 0.397724184789695, 0.358668561675586, 0.391197844815906,
0.191951038548723
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(RNG)`,
c(
0.652160302469174, 0.675755108193839, 1.1335399485195, 0.861249796972752,
0.327165018103644
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(IQR)`,
c(
1.30041734700364, 1.15052496357359, 3.81745140187305, 1.44042831439479,
0.386983932227589
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(STD)`,
c(
1.43814486544896, 1.50905226589782, 2.60289783098973, 1.81516488970619,
0.624379332243931
),
tolerance = 1e-5
)
expect_equal(grouped_res$`NRMSE(AVG)`,
c(0.967171509741637, 0.93906108387384, 0.652360444408903, 0.79060841208794,
0.517217611295255),
tolerance = 1e-5
)
expect_equal(grouped_res$RMSLE,
c(
0.2886413430748, 0.366211402088592, 0.314237354222149, 0.304503365190871,
0.166835911010467
),
tolerance = 1e-5
)
expect_equal(grouped_res$MALE,
c(
0.263279042673231, 0.281636667412819, 0.235196273801541, 0.260569609060969,
0.137188700334378
),
tolerance = 1e-5
)
expect_equal(grouped_res$RAE,
c(
1.76112392170928, 1.42859014846979, 2.71964745212324, 2.02383072091832,
0.598062880532025
),
tolerance = 1e-5
)
expect_equal(grouped_res$RSE,
c(
2.75768087202294, 3.03631832161513, 9.03343615809475, 4.39309810242944,
0.51979940071117
),
tolerance = 1e-5
)
expect_equal(grouped_res$RRSE,
c(
1.66062665040127, 1.74250346387464, 3.00556752679003, 2.09597187539085,
0.720971151094945
),
tolerance = 1e-5
)
expect_equal(grouped_res$MAPE,
c(
1.19356123357169, 0.979598260014201, 0.432928785595697, 0.665919069119316,
1.34479362791513
),
tolerance = 1e-5
)
expect_equal(grouped_res$MSE,
c(
0.203911534107247, 0.269423294953783, 0.225637361253137, 0.206923596206982,
0.0548208716528769
),
tolerance = 1e-5
)
expect_equal(grouped_res$TAE,
c(
1.62834519706666, 1.59089673915878, 1.43467424670234, 1.56479137926362,
0.767804154194891
),
tolerance = 1e-5
)
expect_equal(grouped_res$TSE,
c(
0.815646136428989, 1.07769317981513, 0.90254944501255, 0.827694384827926,
0.219283486611508
),
tolerance = 1e-5
)
})
test_that("call_evaluate_residuals() creates NA results correcly", {
xpectr::set_test_seed(1)
df <- data.frame("t" = runif(20), "p" = runif(20), "grp" = rep(1:5, 4))
na_tibble <- tibble::tibble(
"grp" = 1:5,
"RMSE" = NA,
"MAE" = NA,
"TSE" = NA,
"NRMSE(RNG)" = NA,
"NRMSE(IQR)" = NA
)
expect_equal(
call_evaluate_residuals(
data = df, target_col = "t",
prediction_col = "p",
metrics = c("RMSE", "MAE", "TSE", "NRMSE(RNG)", "NRMSE(IQR)"),
return_nas = TRUE
),
na_tibble[1, 2:6]
)
expect_equal(
call_evaluate_residuals(
data = df %>% dplyr::group_by(grp),
target_col = "t",
prediction_col = "p",
metrics = c("RMSE", "MAE", "TSE", "NRMSE(RNG)", "NRMSE(IQR)"),
return_nas = TRUE
),
na_tibble
)
})
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