Nothing
tests/testthat/test_confusion_matrix.R
In cvms: Cross-Validation for Model Selection
library(cvms)
context("confusion_matrix")
# Diagnosis by score
test_that("Binomial confusion_matrix() works", {
xpectr::set_test_seed(41)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:1, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
cm[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
## Testing 'cm[["Confusion Matrix"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm[["Confusion Matrix"]][[1]]),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Confusion Matrix"]][[1]][["Prediction"]],
c("0", "1", "0", "1"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Target"]],
c("0", "0", "1", "1"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Pos_0"]],
c("TP", "FN", "FP", "TN"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Pos_1"]],
c("TN", "FP", "FN", "TP"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["N"]],
c(15, 2, 7, 26),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm[["Confusion Matrix"]][[1]]),
c("Prediction", "Target", "Pos_0", "Pos_1", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "character", "character", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "character", "character", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm[["Confusion Matrix"]][[1]]),
4:5)
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm[["Confusion Matrix"]][[1]])),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Confusion Matrix"]][[1]]' ####
# TODO test the table
# cm[["Table"]][[1]]
})
test_that("Multinomial confusion_matrix() works", {
xpectr::set_test_seed(41)
targets <- sample(1:4, 50, replace = TRUE)
possible_preds <- sample(1:4, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.72,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
0.81353,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
0.70055,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.71736,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.90969,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.70208,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.90569,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.60867,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62725,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.18,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.25,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.25,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
## Testing 'cm[["Confusion Matrix"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm[["Confusion Matrix"]][[1]]),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Confusion Matrix"]][[1]][["Prediction"]],
c("1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", "1",
"2", "3", "4"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Target"]],
c("1", "1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3", "4",
"4", "4", "4"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["N"]],
c(7, 1, 1, 0, 2, 5, 0, 1, 0, 2, 12, 1, 4, 1, 1, 12),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm[["Confusion Matrix"]][[1]]),
c("Prediction", "Target", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm[["Confusion Matrix"]][[1]]),
c(16L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm[["Confusion Matrix"]][[1]])),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Confusion Matrix"]][[1]]' ####
## Testing 'cm[["Class Level Results"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- cm[["Class Level Results"]][[1]]
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Class"]],
c("1", "2", "3", "4"),
fixed = TRUE)
expect_equal(
output_19148[["Support"]],
c(9, 8, 15, 18),
tolerance = 1e-4)
expect_equal(
output_19148[["Balanced Accuracy"]],
c(0.81572, 0.76488, 0.87143, 0.80208),
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
c(0.77778, 0.625, 0.8, 0.66667),
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
c(0.85366, 0.90476, 0.94286, 0.9375),
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
c(0.53846, 0.55556, 0.85714, 0.85714),
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
c(0.94595, 0.92683, 0.91667, 0.83333),
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
c(0.63636, 0.58824, 0.82759, 0.75),
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
c(0.53811, 0.50425, 0.75728, 0.63504),
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
c(0.18, 0.16, 0.3, 0.36),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
c(0.14, 0.1, 0.24, 0.24),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
c(0.26, 0.18, 0.28, 0.28),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("Class", "Support", "Confusion Matrix", "Table", "Balanced Accuracy",
"Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"F1", "Kappa", "Prevalence", "Detection Rate", "Detection Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("character", "integer", "list", "list", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("character", "integer", "list", "list", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(4L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Class Level Results"]][[1]]' ####
# TODO Test table
# cm[["Table"]][[1]]
})
test_that("different levels in predictions and targets works in confusion_matrix()", {
xpectr::set_test_seed(42)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:2, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.76,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.90366,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.60317,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.86207,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.45507,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62823,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.25333,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.33333,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.33333,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
cm <- confusion_matrix(preds, targets)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.76,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
0.73262,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.60317,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.86207,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.90366,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.45507,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62823,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.25333,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.33333,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.33333,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
})
test_that("fuzzing confusion_matrix()", {
xpectr::set_test_seed(41)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:1, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
# Generate expectations for 'confusion_matrix'
# Tip: comment out the gxs_function() call
# so it is easy to regenerate the tests
xpectr::set_test_seed(42)
# xpectr::gxs_function(
# fn = confusion_matrix,
# args_values = list(
# "targets" = list(targets, NA, 1, integer(0)),
# "predictions" = list(preds, NA, 1, integer(0)),
# "metrics" = list(list(), binomial_metrics(accuracy = FALSE), "hej", NA),
# "positive" = list(2, 1),
# "c_levels" = list(NULL, c(0,1), c("0","1"), c(3,4), c(0,1,2,3)),
# "do_one_vs_all" = list(TRUE, FALSE, NA),
# "parallel" = list(FALSE, NULL)
# ),
# indentation = 2
# )
## Testing 'confusion_matrix' ####
## Initially generated by xpectr
# Testing different combinations of argument values
# Testing confusion_matrix(targets = targets, predicti...
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19148),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19148[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19148[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = NA, predictions =...
# Changed from baseline: targets = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19370 <- xpectr::capture_side_effects(confusion_matrix(targets = NA, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19370[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'targets': Contains missing values (element 1)."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19370[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = 1, predictions = ...
# Changed from baseline: targets = 1
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12861 <- xpectr::capture_side_effects(confusion_matrix(targets = 1, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_12861[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12861[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = integer(0), predi...
# Changed from baseline: targets = integer(0)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18304 <- xpectr::capture_side_effects(confusion_matrix(targets = integer(0), predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_18304[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18304[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = NULL, predictions...
# Changed from baseline: targets = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_16417 <- xpectr::capture_side_effects(confusion_matrix(targets = NULL, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_16417[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'targets': Must be of type 'vector', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_16417[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15190 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = NA, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15190[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'predictions': Contains missing values (element 1)."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15190[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = 1
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17365 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = 1, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17365[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17365[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = integer(0)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11346 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = integer(0), metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11346[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11346[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_16569 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = NULL, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_16569[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'predictions': Must be of type 'vector', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_16569[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = binomial_me...
xpectr::set_test_seed(42)
# Assigning output
output_17050 <- confusion_matrix(targets = targets, predictions = preds, metrics = binomial_metrics(accuracy = FALSE), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_17050),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_17050[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_17050[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_17050[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_17050[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_17050[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_17050[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_17050[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_17050[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_17050[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_17050[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_17050[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_17050[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_17050),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_17050),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_17050),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_17050),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_17050)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = "hej"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14577 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = "hej", positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14577[['error']]),
xpectr::strip("1 assertions failed:\n * 'metrics' contained unknown metric name: hej."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14577[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17191 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = NA, positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17191[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_list(metrics): Must be of type 'list', not 'logical'\n * checkmate::check_character(metrics): Contains missing values (element 1)"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17191[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19346 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = NULL, positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19346[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_list(metrics): Must be of type 'list', not 'NULL'\n * checkmate::check_character(metrics): Must be of type 'character', not 'NULL'"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19346[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: positive = 1
xpectr::set_test_seed(42)
# Assigning output
output_12554 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 1, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_12554),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_12554[["Positive Class"]],
"0",
fixed = TRUE)
expect_equal(
output_12554[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_12554[["F1"]],
0.76923,
tolerance = 1e-4)
expect_equal(
output_12554[["Sensitivity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_12554[["Specificity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_12554[["Pos Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_12554[["Neg Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_12554[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_12554[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_12554[["Detection Rate"]],
0.3,
tolerance = 1e-4)
expect_equal(
output_12554[["Detection Prevalence"]],
0.44,
tolerance = 1e-4)
expect_equal(
output_12554[["Prevalence"]],
0.34,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_12554),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_12554),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_12554),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_12554),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_12554)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: positive = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14622 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = NULL, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14622[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_choice(positive): Must be a subset of {'1','2'}, not 'NULL'\n * checkmate::check_string(positive): Must be of type 'string', not 'NULL'"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14622[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(0, 1)
xpectr::set_test_seed(42)
# Assigning output
output_19400 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(0, 1), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19400),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19400[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19400[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19400[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19400[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19400[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19400[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19400[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19400[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19400[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19400[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19400[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19400[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19400),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19400),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19400),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19400),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19400)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c("0", "1")
xpectr::set_test_seed(42)
# Assigning output
output_19782 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c("0", "1"), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19782),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19782[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19782[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19782[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19782[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19782[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19782[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19782[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19782[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19782[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19782[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19782[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19782[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19782),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19782),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19782),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19782),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19782)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(3, 4)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11174 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(3, 4), do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11174[['error']]),
xpectr::strip("1 assertions failed:\n * 'c_levels' does not contain all the levels in 'predictions' and 'targets'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11174[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(0, 1, 2, 3)
xpectr::set_test_seed(42)
# Assigning output
output_14749 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(0, 1, 2, 3), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_14749),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_14749[["Overall Accuracy"]],
0.82,
tolerance = 1e-4)
expect_equal(
output_14749[["Balanced Accuracy"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Sensitivity"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Specificity"]],
0.91756,
tolerance = 1e-4)
expect_equal(
output_14749[["Pos Pred Value"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Neg Pred Value"]],
0.9026,
tolerance = 1e-4)
expect_equal(
output_14749[["Kappa"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_14749[["Detection Rate"]],
0.205,
tolerance = 1e-4)
expect_equal(
output_14749[["Detection Prevalence"]],
0.25,
tolerance = 1e-4)
expect_equal(
output_14749[["Prevalence"]],
0.25,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_14749),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_14749),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_14749),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_14749),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_14749)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = FALSE
xpectr::set_test_seed(42)
# Assigning output
output_15603 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = FALSE, parallel = FALSE)
# Testing class
expect_equal(
class(output_15603),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_15603[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_15603[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_15603[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_15603[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_15603[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_15603[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_15603[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_15603[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_15603[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_15603[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_15603[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_15603[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_15603),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_15603),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_15603),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_15603),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_15603)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19040 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = NA, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19040[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'do_one_vs_all': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19040[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11387 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = NULL, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11387[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'do_one_vs_all': Must be of type 'logical flag', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11387[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: parallel = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19888 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = NULL), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19888[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'parallel': Must be of type 'logical flag', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19888[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'confusion_matrix' ####
#
})
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library(cvms)
context("confusion_matrix")
# Diagnosis by score
test_that("Binomial confusion_matrix() works", {
xpectr::set_test_seed(41)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:1, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
cm[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
## Testing 'cm[["Confusion Matrix"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm[["Confusion Matrix"]][[1]]),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Confusion Matrix"]][[1]][["Prediction"]],
c("0", "1", "0", "1"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Target"]],
c("0", "0", "1", "1"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Pos_0"]],
c("TP", "FN", "FP", "TN"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Pos_1"]],
c("TN", "FP", "FN", "TP"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["N"]],
c(15, 2, 7, 26),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm[["Confusion Matrix"]][[1]]),
c("Prediction", "Target", "Pos_0", "Pos_1", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "character", "character", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "character", "character", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm[["Confusion Matrix"]][[1]]),
4:5)
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm[["Confusion Matrix"]][[1]])),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Confusion Matrix"]][[1]]' ####
# TODO test the table
# cm[["Table"]][[1]]
})
test_that("Multinomial confusion_matrix() works", {
xpectr::set_test_seed(41)
targets <- sample(1:4, 50, replace = TRUE)
possible_preds <- sample(1:4, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.72,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
0.81353,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
0.70055,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.71736,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.90969,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.70208,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.90569,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.60867,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62725,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.18,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.25,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.25,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
## Testing 'cm[["Confusion Matrix"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm[["Confusion Matrix"]][[1]]),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Confusion Matrix"]][[1]][["Prediction"]],
c("1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", "1",
"2", "3", "4"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["Target"]],
c("1", "1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3", "4",
"4", "4", "4"),
fixed = TRUE)
expect_equal(
cm[["Confusion Matrix"]][[1]][["N"]],
c(7, 1, 1, 0, 2, 5, 0, 1, 0, 2, 12, 1, 4, 1, 1, 12),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm[["Confusion Matrix"]][[1]]),
c("Prediction", "Target", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm[["Confusion Matrix"]][[1]]),
c("character", "character", "integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm[["Confusion Matrix"]][[1]]),
c(16L, 3L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm[["Confusion Matrix"]][[1]])),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Confusion Matrix"]][[1]]' ####
## Testing 'cm[["Class Level Results"]][[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- cm[["Class Level Results"]][[1]]
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Class"]],
c("1", "2", "3", "4"),
fixed = TRUE)
expect_equal(
output_19148[["Support"]],
c(9, 8, 15, 18),
tolerance = 1e-4)
expect_equal(
output_19148[["Balanced Accuracy"]],
c(0.81572, 0.76488, 0.87143, 0.80208),
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
c(0.77778, 0.625, 0.8, 0.66667),
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
c(0.85366, 0.90476, 0.94286, 0.9375),
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
c(0.53846, 0.55556, 0.85714, 0.85714),
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
c(0.94595, 0.92683, 0.91667, 0.83333),
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
c(0.63636, 0.58824, 0.82759, 0.75),
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
c(0.53811, 0.50425, 0.75728, 0.63504),
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
c(0.18, 0.16, 0.3, 0.36),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
c(0.14, 0.1, 0.24, 0.24),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
c(0.26, 0.18, 0.28, 0.28),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("Class", "Support", "Confusion Matrix", "Table", "Balanced Accuracy",
"Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"F1", "Kappa", "Prevalence", "Detection Rate", "Detection Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("character", "integer", "list", "list", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("character", "integer", "list", "list", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(4L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'cm[["Class Level Results"]][[1]]' ####
# TODO Test table
# cm[["Table"]][[1]]
})
test_that("different levels in predictions and targets works in confusion_matrix()", {
xpectr::set_test_seed(42)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:2, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
cm <- confusion_matrix(targets, preds)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.76,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.90366,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
0.60317,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.86207,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.45507,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62823,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.25333,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.33333,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.33333,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
cm <- confusion_matrix(preds, targets)
## Testing 'cm' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(cm),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
cm[["Overall Accuracy"]],
0.76,
tolerance = 1e-4)
expect_equal(
cm[["Balanced Accuracy"]],
0.73262,
tolerance = 1e-4)
expect_equal(
cm[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Sensitivity"]],
0.60317,
tolerance = 1e-4)
expect_equal(
cm[["Specificity"]],
0.86207,
tolerance = 1e-4)
expect_equal(
cm[["Pos Pred Value"]],
NaN,
tolerance = 1e-4)
expect_equal(
cm[["Neg Pred Value"]],
0.90366,
tolerance = 1e-4)
expect_equal(
cm[["Kappa"]],
0.45507,
tolerance = 1e-4)
expect_equal(
cm[["MCC"]],
0.62823,
tolerance = 1e-4)
expect_equal(
cm[["Detection Rate"]],
0.25333,
tolerance = 1e-4)
expect_equal(
cm[["Detection Prevalence"]],
0.33333,
tolerance = 1e-4)
expect_equal(
cm[["Prevalence"]],
0.33333,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(cm),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(cm),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(cm),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(cm),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(cm)),
character(0),
fixed = TRUE)
## Finished testing 'cm' ####
})
test_that("fuzzing confusion_matrix()", {
xpectr::set_test_seed(41)
targets <- sample(0:1, 50, replace = TRUE)
possible_preds <- sample(0:1, 50, replace = TRUE)
preds <- ifelse(runif(50) > 0.7, possible_preds, targets )
# Generate expectations for 'confusion_matrix'
# Tip: comment out the gxs_function() call
# so it is easy to regenerate the tests
xpectr::set_test_seed(42)
# xpectr::gxs_function(
# fn = confusion_matrix,
# args_values = list(
# "targets" = list(targets, NA, 1, integer(0)),
# "predictions" = list(preds, NA, 1, integer(0)),
# "metrics" = list(list(), binomial_metrics(accuracy = FALSE), "hej", NA),
# "positive" = list(2, 1),
# "c_levels" = list(NULL, c(0,1), c("0","1"), c(3,4), c(0,1,2,3)),
# "do_one_vs_all" = list(TRUE, FALSE, NA),
# "parallel" = list(FALSE, NULL)
# ),
# indentation = 2
# )
## Testing 'confusion_matrix' ####
## Initially generated by xpectr
# Testing different combinations of argument values
# Testing confusion_matrix(targets = targets, predicti...
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19148),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19148[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19148[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19148),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19148),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19148),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = NA, predictions =...
# Changed from baseline: targets = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19370 <- xpectr::capture_side_effects(confusion_matrix(targets = NA, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19370[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'targets': Contains missing values (element 1)."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19370[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = 1, predictions = ...
# Changed from baseline: targets = 1
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_12861 <- xpectr::capture_side_effects(confusion_matrix(targets = 1, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_12861[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_12861[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = integer(0), predi...
# Changed from baseline: targets = integer(0)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_18304 <- xpectr::capture_side_effects(confusion_matrix(targets = integer(0), predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_18304[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_18304[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = NULL, predictions...
# Changed from baseline: targets = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_16417 <- xpectr::capture_side_effects(confusion_matrix(targets = NULL, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_16417[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'targets': Must be of type 'vector', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_16417[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_15190 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = NA, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_15190[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'predictions': Contains missing values (element 1)."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_15190[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = 1
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17365 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = 1, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17365[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17365[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = integer(0)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11346 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = integer(0), metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11346[['error']]),
xpectr::strip("1 assertions failed:\n * 'targets' and 'predictions' must have same length."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11346[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: predictions = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_16569 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = NULL, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_16569[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'predictions': Must be of type 'vector', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_16569[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = binomial_me...
xpectr::set_test_seed(42)
# Assigning output
output_17050 <- confusion_matrix(targets = targets, predictions = preds, metrics = binomial_metrics(accuracy = FALSE), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_17050),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_17050[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_17050[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_17050[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_17050[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_17050[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_17050[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_17050[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_17050[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_17050[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_17050[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_17050[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_17050[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_17050),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_17050),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_17050),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_17050),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_17050)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = "hej"
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14577 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = "hej", positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14577[['error']]),
xpectr::strip("1 assertions failed:\n * 'metrics' contained unknown metric name: hej."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14577[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_17191 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = NA, positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_17191[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_list(metrics): Must be of type 'list', not 'logical'\n * checkmate::check_character(metrics): Contains missing values (element 1)"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_17191[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: metrics = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19346 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = NULL, positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19346[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_list(metrics): Must be of type 'list', not 'NULL'\n * checkmate::check_character(metrics): Must be of type 'character', not 'NULL'"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19346[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: positive = 1
xpectr::set_test_seed(42)
# Assigning output
output_12554 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 1, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_12554),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_12554[["Positive Class"]],
"0",
fixed = TRUE)
expect_equal(
output_12554[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_12554[["F1"]],
0.76923,
tolerance = 1e-4)
expect_equal(
output_12554[["Sensitivity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_12554[["Specificity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_12554[["Pos Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_12554[["Neg Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_12554[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_12554[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_12554[["Detection Rate"]],
0.3,
tolerance = 1e-4)
expect_equal(
output_12554[["Detection Prevalence"]],
0.44,
tolerance = 1e-4)
expect_equal(
output_12554[["Prevalence"]],
0.34,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_12554),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_12554),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_12554),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_12554),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_12554)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: positive = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_14622 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = NULL, c_levels = NULL, do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_14622[['error']]),
xpectr::strip("Assertion failed. One of the following must apply:\n * checkmate::check_choice(positive): Must be a subset of {'1','2'}, not 'NULL'\n * checkmate::check_string(positive): Must be of type 'string', not 'NULL'"),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_14622[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(0, 1)
xpectr::set_test_seed(42)
# Assigning output
output_19400 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(0, 1), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19400),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19400[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19400[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19400[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19400[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19400[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19400[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19400[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19400[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19400[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19400[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19400[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19400[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19400),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19400),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19400),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19400),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19400)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c("0", "1")
xpectr::set_test_seed(42)
# Assigning output
output_19782 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c("0", "1"), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_19782),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19782[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_19782[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_19782[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_19782[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_19782[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_19782[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_19782[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_19782[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_19782[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_19782[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_19782[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_19782[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19782),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_19782),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_19782),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19782),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19782)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(3, 4)
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11174 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(3, 4), do_one_vs_all = TRUE, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11174[['error']]),
xpectr::strip("1 assertions failed:\n * 'c_levels' does not contain all the levels in 'predictions' and 'targets'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11174[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: c_levels = c(0, 1, 2, 3)
xpectr::set_test_seed(42)
# Assigning output
output_14749 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = c(0, 1, 2, 3), do_one_vs_all = TRUE, parallel = FALSE)
# Testing class
expect_equal(
class(output_14749),
c("cfm_results", "cfm_multinomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_14749[["Overall Accuracy"]],
0.82,
tolerance = 1e-4)
expect_equal(
output_14749[["Balanced Accuracy"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["F1"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Sensitivity"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Specificity"]],
0.91756,
tolerance = 1e-4)
expect_equal(
output_14749[["Pos Pred Value"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["Neg Pred Value"]],
0.9026,
tolerance = 1e-4)
expect_equal(
output_14749[["Kappa"]],
NaN,
tolerance = 1e-4)
expect_equal(
output_14749[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_14749[["Detection Rate"]],
0.205,
tolerance = 1e-4)
expect_equal(
output_14749[["Detection Prevalence"]],
0.25,
tolerance = 1e-4)
expect_equal(
output_14749[["Prevalence"]],
0.25,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_14749),
c("Confusion Matrix", "Table", "Class Level Results", "Overall Accuracy",
"Balanced Accuracy", "F1", "Sensitivity", "Specificity", "Pos Pred Value",
"Neg Pred Value", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_14749),
c("list", "list", "list", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_14749),
c("list", "list", "list", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_14749),
c(1L, 15L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_14749)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = FALSE
xpectr::set_test_seed(42)
# Assigning output
output_15603 <- confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = FALSE, parallel = FALSE)
# Testing class
expect_equal(
class(output_15603),
c("cfm_results", "cfm_binomial", "tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_15603[["Positive Class"]],
"1",
fixed = TRUE)
expect_equal(
output_15603[["Balanced Accuracy"]],
0.83512,
tolerance = 1e-4)
expect_equal(
output_15603[["F1"]],
0.85246,
tolerance = 1e-4)
expect_equal(
output_15603[["Sensitivity"]],
0.78788,
tolerance = 1e-4)
expect_equal(
output_15603[["Specificity"]],
0.88235,
tolerance = 1e-4)
expect_equal(
output_15603[["Pos Pred Value"]],
0.92857,
tolerance = 1e-4)
expect_equal(
output_15603[["Neg Pred Value"]],
0.68182,
tolerance = 1e-4)
expect_equal(
output_15603[["Kappa"]],
0.62562,
tolerance = 1e-4)
expect_equal(
output_15603[["MCC"]],
0.63961,
tolerance = 1e-4)
expect_equal(
output_15603[["Detection Rate"]],
0.52,
tolerance = 1e-4)
expect_equal(
output_15603[["Detection Prevalence"]],
0.56,
tolerance = 1e-4)
expect_equal(
output_15603[["Prevalence"]],
0.66,
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_15603),
c("Confusion Matrix", "Table", "Positive Class", "Balanced Accuracy",
"F1", "Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(output_15603),
c("list", "list", "character", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(output_15603),
c("list", "list", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_15603),
c(1L, 14L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_15603)),
character(0),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = NA
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19040 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = NA, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19040[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'do_one_vs_all': May not be NA."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19040[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: do_one_vs_all = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_11387 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = NULL, parallel = FALSE), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_11387[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'do_one_vs_all': Must be of type 'logical flag', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_11387[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
# Testing confusion_matrix(targets = targets, predicti...
# Changed from baseline: parallel = NULL
xpectr::set_test_seed(42)
# Testing side effects
# Assigning side effects
side_effects_19888 <- xpectr::capture_side_effects(confusion_matrix(targets = targets, predictions = preds, metrics = list(), positive = 2, c_levels = NULL, do_one_vs_all = TRUE, parallel = NULL), reset_seed = TRUE)
expect_equal(
xpectr::strip(side_effects_19888[['error']]),
xpectr::strip("1 assertions failed:\n * Variable 'parallel': Must be of type 'logical flag', not 'NULL'."),
fixed = TRUE)
expect_equal(
xpectr::strip(side_effects_19888[['error_class']]),
xpectr::strip(c("simpleError", "error", "condition")),
fixed = TRUE)
## Finished testing 'confusion_matrix' ####
#
})
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