tests/testthat/test_baseline.R
In LudvigOlsen/cvms: Cross-Validation for Model Selection
library(cvms)
context("baseline()")
test_that("binomial evaluations are correct in baseline()", {
# We're too near the test time limit on CRAN
# So skipping this and relying on local and github actions
# to test this
testthat::skip_on_cran()
xpectr::set_test_seed(1)
binom_baseline <- baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
metrics = "all",
parallel = FALSE
)
binom_baseline_summ <- binom_baseline$summarized_metrics
binom_baseline_reval <- binom_baseline$random_evaluations
expect_equal(
colnames(binom_baseline_summ),
c("Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI",
"Upper CI", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score")
)
expect_equal(
colnames(binom_baseline_reval),
c("Balanced Accuracy", "Accuracy", "F1", "Sensitivity", "Specificity",
"Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI", "Upper CI",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score", "Predictions", "ROC", "Confusion Matrix", "Process",
"Dependent")
)
# Summarized results
expect_equal(binom_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_0", "All_1"
))
expect_equal(binom_baseline_summ$`Balanced Accuracy`,
c(
0.493055555555556, 0.472222222222222, 0.0940282222758792, 0.111111111111111,
0.680555555555556, 0.347222222222222, 0, 0, 0.5, 0.5
),
tolerance = 1e-4
)
expect_equal(binom_baseline_summ$Accuracy,
c(
0.486666666666667, 0.466666666666667, 0.102077191803163, 0.116666666666667,
0.666666666666667, 0.3, 0, 0, 0.4, 0.6
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$F1,
c(
0.5080706, 0.5294118, 0.1486011, 0.1303922, 0.6875000,
0.1600000, 0.0000000, 0.0000000, NA, 0.7500000
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Pos Pred Value`,
c(
0.5751821, 0.5729167, 0.1299251, 0.1009191,
0.7857143, 0.2857143, 0.0, 0.0, NaN, 0.60
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Neg Pred Value`,
c(
0.39934657, 0.37301587, 0.07978087, 0.11401099,
0.5625, 0.30434783, 0.0, 0.0, 0.40, NaN
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$AUC,
c(
0.54907407, 0.54398148, 0.08210109, 0.09143519,
0.69907407, 0.43055556, 0.0, 0.0, 0.50, 0.50
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Lower CI`,
c(
0.324540013310847, 0.323088934387213, 0.0886239631372627, 0.110594163133731,
0.484635549804418, 0.20545074066643, 0, 0, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Upper CI`,
c(
0.773608134837302, 0.7593316803908, 0.0766521660833089, 0.0815468441468371,
0.91351259834373, 0.655660370444682, 0, 0, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$Kappa,
c(
-0.0100961224305358, -0.0533610533610534, 0.174558224749766,
0.215975564605702, 0.342105263157895, -0.265060240963855, 0,
0, 0, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$MCC,
c(
-0.0193021509301018, -0.0548047433081375, 0.194735989045363,
0.218543873422503, 0.354604071633488, -0.353919198648283, 0,
0, 0, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Detection Rate`,
c(
0.276666666666667, 0.3, 0.0916919157145678, 0.0916666666666667,
0.366666666666667, 0.0666666666666667, 0, 0, 0, 0.6
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Detection Prevalence`,
c(
0.466666666666667, 0.5, 0.0993807989999906, 0.116666666666667,
0.566666666666667, 0.233333333333333, 0, 0, 0, 1
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$Prevalence,
c(0.6, 0.6, 0, 0, 0.6, 0.6, 0, 0, 0.6, 0.6),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Neg Rate`,
c(
0.538888888888889, 0.5, 0.15281985952428, 0.152777777777778,
0.888888888888889, 0.388888888888889, 0, 0, 1, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Pos Rate`,
c(
0.475, 0.5, 0.104305463086214, 0.145833333333333, 0.583333333333333,
0.25, 0, 0, 0, 1
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Discovery Rate`,
c(
0.424817927170868, 0.427083333333333, 0.129925068392218, 0.100919117647059,
0.714285714285714, 0.214285714285714, 0, 0, NaN, 0.4
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Omission Rate`,
c(
0.600653434729522, 0.626984126984127, 0.0797808713464425, 0.114010989010989,
0.695652173913043, 0.4375, 0, 0, 0.6, NaN
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Threat Score`,
c(0.351489648033126, 0.36, 0.122896613634581, 0.121992753623188,
0.523809523809524, 0.0869565217391304, 0, 0, 0, 0.6),
tolerance = 1e-5
)
# The random evaluations
expect_equal(binom_baseline_reval$`Balanced Accuracy`,
c(
0.6805556, 0.5277778, 0.4861111, 0.4305556, 0.5555556,
0.4583333, 0.4583333, 0.5694444, 0.4166667, 0.3472222
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Accuracy,
c(
0.666666666666667, 0.533333333333333, 0.466666666666667, 0.433333333333333,
0.566666666666667, 0.466666666666667, 0.466666666666667, 0.566666666666667,
0.4, 0.3
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$F1,
c(
0.6875, 0.5882353, 0.4666667, 0.4848485, 0.6285714,
0.5294118, 0.5294118, 0.6060606, 0.40, 0.160
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Sensitivity,
c(
0.611111111111111, 0.555555555555556, 0.388888888888889, 0.444444444444444,
0.611111111111111, 0.5, 0.5, 0.555555555555556, 0.333333333333333,
0.111111111111111
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Specificity,
c(
0.75, 0.5, 0.583333333333333, 0.416666666666667, 0.5, 0.416666666666667,
0.416666666666667, 0.583333333333333, 0.5, 0.583333333333333
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Pos Pred Value`,
c(
0.785714285714286, 0.625, 0.583333333333333, 0.533333333333333,
0.647058823529412, 0.5625, 0.5625, 0.666666666666667, 0.5, 0.285714285714286
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Neg Pred Value`,
c(
0.5625, 0.428571428571429, 0.388888888888889, 0.333333333333333,
0.461538461538462, 0.357142857142857, 0.357142857142857, 0.466666666666667,
0.333333333333333, 0.304347826086957
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$AUC,
c(
0.6990741, 0.5925926, 0.6527778, 0.500, 0.5555556,
0.4305556, 0.5601852, 0.5324074, 0.4907407, 0.4768519
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Lower CI`,
c(
0.484635549804418, 0.368049842933427, 0.436226588192728, 0.275453328641993,
0.325144150211566, 0.20545074066643, 0.345488105840725, 0.32103371856286,
0.243935884488029, 0.239982223766291
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Upper CI`,
c(
0.91351259834373, 0.817135342251759, 0.869328967362827, 0.724546671358007,
0.785966960899545, 0.655660370444682, 0.774882264529645, 0.743781096251955,
0.737545596993452, 0.713721479937413
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Kappa,
c(
0.342105263157895, 0.054054054054054, -0.0256410256410256,
-0.133333333333333, 0.10958904109589, -0.0810810810810812, -0.0810810810810812,
0.133333333333333, -0.153846153846154, -0.265060240963855
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$MCC,
c(
0.354604071633488, 0.0545544725589981, -0.0277777777777778,
-0.136082763487954, 0.109847007276218, -0.0818317088384971, -0.0818317088384971,
0.136082763487954, -0.166666666666667, -0.353919198648283
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Detection Rate`,
c(
0.366666666666667, 0.333333333333333, 0.233333333333333, 0.266666666666667,
0.366666666666667, 0.3, 0.3, 0.333333333333333, 0.2, 0.0666666666666667
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Detection Prevalence`,
c(
0.466666666666667, 0.533333333333333, 0.4, 0.5, 0.566666666666667,
0.533333333333333, 0.533333333333333, 0.5, 0.4, 0.233333333333333
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Prevalence,
c(0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Neg Rate`,
c(
0.388888888888889, 0.444444444444444, 0.611111111111111, 0.555555555555556,
0.388888888888889, 0.5, 0.5, 0.444444444444444, 0.666666666666667,
0.888888888888889
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Pos Rate`,
c(
0.25, 0.5, 0.416666666666667, 0.583333333333333, 0.5, 0.583333333333333,
0.583333333333333, 0.416666666666667, 0.5, 0.416666666666667
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Discovery Rate`,
c(
0.214285714285714, 0.375, 0.416666666666667, 0.466666666666667,
0.352941176470588, 0.4375, 0.4375, 0.333333333333333, 0.5, 0.714285714285714
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Omission Rate`,
c(
0.4375, 0.571428571428571, 0.611111111111111, 0.666666666666667,
0.538461538461538, 0.642857142857143, 0.642857142857143, 0.533333333333333,
0.666666666666667, 0.695652173913043
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Threat Score`,
c(0.523809523809524, 0.416666666666667, 0.304347826086957, 0.32,
0.458333333333333, 0.36, 0.36, 0.434782608695652, 0.25, 0.0869565217391304
),
tolerance = 1e-3
)
expect_equal(
binom_baseline_reval$Dependent,
rep("diagnosis", 10)
)
expect_equal(length(binom_baseline_reval$Predictions), 10)
all_predictions <- dplyr::bind_rows(binom_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 30))
)
expect_equal(sum(all_predictions$Target), 180)
expect_equal(sum(as.numeric(all_predictions$`Predicted Class`)), 140)
expect_equal(sum(all_predictions$Prediction), 147.0334, tolerance = 1e-3)
## Testing 'all_predictions' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_predictions),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(all_predictions[["Repetition"]], n = 30),
c(1, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 7, 7, 7, 7, 8, 8, 9, 9,
9, 9, 9, 9, 10, 10, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Target"]], n = 30),
c(1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Prediction"]], n = 30),
c(0.65167, 0.18622, 0.72371, 0.41127, 0.33377, 0.20269, 0.12937,
0.49559, 0.03554, 0.6428, 0.60154, 0.45313, 0.07528, 0.54765,
0.38891, 0.51858, 0.12916, 0.64958, 0.25762, 0.42875, 0.13269,
0.60359, 0.27797, 0.75471, 0.16958, 0.29865, 0.18123, 0.02779,
0.22131, 0.92925),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Predicted Class"]], n = 30),
c("1", "0", "1", "0", "0", "0", "0", "0", "0", "1", "1", "0", "0",
"1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "1", "0",
"0", "0", "0", "0", "1"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_predictions),
c("Repetition", "Target", "Prediction", "Predicted Class"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_predictions),
c("integer", "numeric", "numeric", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_predictions),
c("integer", "double", "double", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_predictions),
c(300L, 4L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_predictions)),
character(0),
fixed = TRUE)
## Finished testing 'all_predictions' ####
all_confmats <- dplyr::bind_rows(binom_baseline_reval$`Confusion Matrix`)
expect_equal(sum(as.numeric(all_confmats$Prediction)), 20, tolerance = 1e-3)
expect_equal(sum(as.numeric(all_confmats$Target)), 20, tolerance = 1e-3)
expect_equal(sum(all_confmats$N), 300, tolerance = 1e-3)
expect_equal(sum(all_confmats$Repetition), 220, tolerance = 1e-3)
## Testing 'all_confmats' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_confmats),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(all_confmats[["Repetition"]], n = 30),
c(1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7,
8, 8, 8, 8, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_confmats[["Prediction"]], n = 30),
c("1", "0", "0", "1", "0", "1", "1", "0", "1", "0", "1", "0", "1",
"0", "0", "1", "0", "1", "0", "1", "0", "0", "1", "0", "1",
"0", "1", "0", "1", "1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Target"]], n = 30),
c("0", "1", "0", "0", "1", "1", "0", "1", "1", "0", "0", "0", "0",
"1", "0", "0", "1", "1", "0", "0", "1", "0", "0", "1", "1",
"1", "1", "0", "0", "1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Pos_0"]], n = 30),
c("FN", "FP", "TP", "FN", "FP", "TN", "FN", "FP", "TN", "TP", "FN",
"TP", "FN", "FP", "TP", "FN", "FP", "TN", "TP", "FN", "FP",
"TP", "FN", "FP", "TN", "FP", "TN", "TP", "FN", "TN"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Pos_1"]], n = 30),
c("FP", "FN", "TN", "FP", "FN", "TP", "FP", "FN", "TP", "TN", "FP",
"TN", "FP", "FN", "TN", "FP", "FN", "TP", "TN", "FP", "FN",
"TN", "FP", "FN", "TP", "FN", "TP", "TN", "FP", "TP"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["N"]], n = 30),
c(3, 7, 6, 6, 8, 10, 5, 11, 7, 5, 7, 6, 6, 7, 5, 7, 9, 9, 5, 7,
9, 7, 5, 8, 10, 12, 6, 7, 5, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(all_confmats),
c("Repetition", "Prediction", "Target", "Pos_0", "Pos_1", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_confmats),
c("integer", "character", "character", "character", "character",
"integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_confmats),
c("integer", "character", "character", "character", "character",
"integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_confmats),
c(40L, 6L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_confmats)),
character(0),
fixed = TRUE)
## Finished testing 'all_confmats' ####
# Test first 'Process'
## Testing 'binom_baseline_reval$Process[[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_19148 <- as.list(binom_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"process_info_binomial",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "list")
# Testing names
expect_equal(
names(output_19148),
c("Target Column", "Prediction Column", "ID Column", "Family", "Classes",
"Positive Class", "Cutoff", "Target Summary", "Probability Summary",
"Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_19148$`Target Column`,
"diagnosis"
)
expect_equal(
output_19148$`Prediction Column`,
"prediction"
)
expect_equal(
output_19148$`ID Column`,
NULL
)
expect_equal(
output_19148$Family,
"Binomial"
)
expect_equal(
output_19148$Classes,
c("0", "1")
)
expect_equal(
output_19148$`Positive Class`,
"1"
)
expect_equal(
output_19148$Cutoff,
0.5
)
expect_equal(
output_19148$`Target Summary`,
list(Total = 30L,
`Class Counts` = c(`0` = 12, `1` = 18))
)
expect_equal(
output_19148$`Probability Summary`,
list(Mean = 0.509552392472203,
Median = 0.44190666731447,
Range = c(0.0133903331588954,0.991906094830483),
SD = 0.295280025751536,
IQR = 0.490849027526565)
)
expect_true(
output_19148$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'binom_baseline_reval$Process[[1]]' ####
## Testing 'as.character(binom_baseline_reval$Process[[1]])' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(binom_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: diagnosis\nPr",
"ediction column: prediction\nFamily / type: Binomial\nClasse",
"s: 0, 1\nPositive class: 1\nCutoff: 0.5\nProbabilities are o",
"f class: 1\nProbabilities < 0.5 are considered: 0\nProbabili",
"ties >= 0.5 are considered: 1\nLocale used when sorting clas",
"s levels (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\nTarget counts: total=30, 0=12, 1=1",
"8\nProbability summary: mean: 0.51, median: 0.442, range: [0",
".013, 0.992], SD: 0.295, IQR: 0.491\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(binom_baseline_reval$Process[[1]])' ####
})
test_that("gaussian evaluations are correct in baseline()", {
# We're too near the test time limit on CRAN
# So skipping this and relying on local and github actions
# to test this
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
n = 10,
family = "gaussian",
metrics = "all",
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
23.548673, 22.631836, 2.151282, 1.774305,
27.814396, 20.671713, 0.0, 0.0, 23.108507
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
20.601647, 20.021429, 1.559585, 1.191468,
23.645833, 18.214286, 0.0, 0.0, 20.370370
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
80.88782, 73.28142, 31.21325, 46.54117,
125.03483, 40.63481, 0.0, 0.0, 156.22826
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(
gaussian_baseline_summ$r2m,
rep(0.0, 9)
)
expect_equal(
gaussian_baseline_summ$r2c,
rep(0.0, 9)
)
expect_equal(gaussian_baseline_summ$AICc,
c(
83.38409, 75.63857, 29.84876, 45.02450,
126.12574, 46.63481, 0.0, 0.0, 157.02826
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
81.27241, 73.52992, 31.93310, 47.55239,
126.31295, 39.85369, 0.0, 0.0, 158.0090
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
26.47565, 22.39047, 20.67171, 24.70268, 23.08974,
22.57248, 22.69119, 22.51107, 22.56734, 27.81440
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
22.63333, 19.83333, 18.21429, 21.38889, 20.35714,
19.97619, 20.06667, 19.92857, 19.97222, 23.64583
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
40.63481, 96.54162, 63.62098, 46.39313, 57.93568,
123.76021, 82.94187, 125.03483, 108.86128, 63.15374
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4085.043, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
expect_equal(
colnames(all_coeffs),
c(
"Repetition", "term", "estimate", "std.error", "conf.level",
"conf.low", "conf.high", "statistic", "df.error", "p.value"
)
)
expect_equal(all_coeffs$Repetition, 1:10)
expect_equal(all_coeffs$estimate,
c(
28.40000, 36.00000, 41.71429, 31.33333, 34.42857,
35.57143, 35.30000, 35.71429, 35.58333, 26.37500
),
tolerance = 1e-3
)
expect_equal(all_coeffs$std.error,
c(
4.718050, 3.448759, 6.985408, 3.702852, 4.654016,
4.834303, 4.176788, 5.059458, 5.762284, 3.688484
),
tolerance = 1e-3
)
expect_equal(all_coeffs$p.value,
c(
3.836776e-03, 4.806737e-07, 9.888062e-04, 3.785030e-04,
3.133274e-04, 5.518161e-06, 1.423837e-05, 8.556549e-06,
6.954980e-05, 1.852572e-04
),
tolerance = 1e-3
)
# Test first process
## Testing 'gaussian_baseline_reval$Process[[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_11521 <- gaussian_baseline_reval$Process[[1]]
# Testing class
expect_equal(
class(output_11521),
"process_info_gaussian",
fixed = TRUE)
# Testing type
expect_type(
output_11521,
type = "list")
# Testing names
expect_equal(
names(output_11521),
c("Target Column", "Prediction Column", "ID Column", "Family", "Target Summary",
"Prediction Summary", "Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_11521$`Target Column`,
"score"
)
expect_equal(
output_11521$`Prediction Column`,
"prediction"
)
expect_equal(
output_11521$`ID Column`,
NULL
)
expect_equal(
output_11521$Family,
"Gaussian"
)
expect_equal(
output_11521$`Target Summary`,
list(Mean = 45.3333333333333,
Median = 51.5,
Range = c(11, 78),
SD = 21.2574407650307,
IQR = 29.25)
)
expect_equal(
output_11521$`Prediction Summary`,
list(Mean = 28.4,
Median = 28.4,
Range = c(28.4, 28.4),
SD = 0,
IQR = 0)
)
expect_true(
output_11521$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'gaussian_baseline_reval$Process[[1]]' ####
## Testing 'as.character(gaussian_baseline_reval$Process...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(gaussian_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: score\nPredic",
"tion column: prediction\nFamily / type: Gaussian\nTarget sum",
"mary: mean: 45.333, median: 51.5, range: [11, 78], SD: 21.25",
"7, IQR: 29.25\nPrediction summary: mean: 28.4, median: 28.4,",
" range: [28.4, 28.4], SD: 0, IQR: 0\nLocale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(gaussian_baseline_reval$Process...' ####
})
test_that("gaussian evaluations of random effects models are correct in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline_random_effects <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
random_effects = "( 1 | session )",
n = 10,
family = "gaussian",
metrics = "all",
REML = TRUE, # Usually FALSE, but TRUE to avoid changing the tests
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline_random_effects$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline_random_effects$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
19.600875, 19.568833, 3.010504, 5.332666,
23.631064, 15.792418, 0.0, 0.0, 18.066222
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
17.231620, 17.259500, 2.284422, 3.206596,
20.680264, 14.117023, 0.0, 0.0, 16.173775
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
74.72007, 68.53657, 29.06934, 44.68871,
115.91591, 37.04749, 0.0, 0.0, 146.98744
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2m,
rep(0.0, 9),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2c,
c(
0.5689284, 0.5931160, 0.1248541, 0.1792077,
0.7194042, 0.3450947, 0.0, 0.0, 0.5092992
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AICc,
c(
82.00007, 74.53657, 24.19749, 39.18656,
118.31591, 54.54786, 0.0, 0.0, 148.70173
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
75.2969607911245, 68.9093092222065, 30.1532384455176, 46.2055462747716,
117.833083604798, 35.875805418616, 0, 0, 149.658560139426
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
23.3023673893311, 20.2831211351759, 18.4653958494823, 23.6310640067372,
19.6708765288482, 15.7924182217171, 19.4667891710134, 16.5250914306427,
15.8422216462345, 23.0294037571561
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
19.9254197349807, 18.1852507167284, 17.0487074806514, 20.6802643236492,
17.0551245625476, 14.2061291677316, 17.4638756853949, 14.8688972771474,
14.1170229613728, 18.7655103243966
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
37.0474916813137, 92.0001975699801, 59.3199306240758, 42.5478613478296,
51.7112998206263, 112.962482318462, 77.7532020941892, 115.915911615952,
99.9577092947807, 57.9846047242755
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Random,
rep("(1|session)", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4030.024, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
## Testing 'all_coeffs' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_coeffs),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
all_coeffs[["Repetition"]],
c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
all_coeffs[["term"]],
c("(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)"),
fixed = TRUE)
expect_equal(
all_coeffs[["estimate"]],
c(29.44832, 8.66773, 8.1377, 34.03974, 8.18275, 9.56568, 40.93218,
11.24335, 15.48873, 29.70897, 7.25112, 6.08707, 31.63809, 11.08484,
6.92283, 37.72826, 16.93924, 11.13578, 34.17823, 9.41796, 10.05082,
35.95068, 15.69245, 12.82093, 36.3069, 17.50155, 12.76391, 27.2041,
9.30371, 6.58837),
tolerance = 1e-4)
expect_equal(
all_coeffs[["std.error"]],
c(7.15631722929513, 8.48017078596171, 3.32220364038992, 5.53120433851552,
5.6642976362218, 2.25389751408461, 8.97275331112187, 11.3857986359196,
5.42155778061848, 4.96158288393548, 5.24814564894732, 2.47708793486519,
7.05688044109226, 6.80599557763085, 2.44460604383921, 10.2738954220409,
9.3566019868576, 2.37405616559033, 6.56119896982287, 7.00187724900024,
2.67291574649631, 9.73185625725407, 9.07113095976679, 2.73321585096119,
10.7798459438635, 9.97743399907886, 3.00837168723259, 5.86883171896131,
5.58366565229798, 2.08319768342238),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.level"]],
c(0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.low"]],
c(-1.34282744691279, 1.27386538040635, 3.65595044257592, 21.5272884598991,
2.10706150007808, 6.02772189002962, 16.0198275271794, 1.54493287924856,
7.79951776760785, 13.9189969554157, 1.75521843225081, 2.74168881162613,
12.0450475999266, 3.32735374149643, 3.4650146393119, 15.1155676968666,
5.73749350704645, 7.33249168749023, 18.6634644982617, 2.19342388622354,
5.96805635026065, 14.5310088047755, 5.0541486113636, 8.44222372952845,
11.9211926362553, 5.72553547803716, 8.04194093825517, 12.1177908826813,
2.86948523148235, 3.54513955526967),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.high"]],
c(60.2394682762468, 58.9776840264608, 18.1135448112533, 46.5521954864459,
31.7776340871192, 15.1802497890285, 65.8445415488026, 81.8241896481276,
30.7584161713846, 45.4989391900944, 29.9556977953848, 13.5144521340807,
51.2311299171999, 36.9283411378221, 13.8312651699865, 60.3409504173574,
50.0110277516517, 16.9117984896506, 49.6930049053992, 40.4381415401269,
16.9266142042435, 57.3703512094978, 48.7229715372203, 19.4707153600945,
60.6926040677994, 53.49790912956, 20.2584602694038, 42.2904153090177,
30.1653776868507, 12.2439623269154),
tolerance = 1e-4)
expect_equal(
all_coeffs[["statistic"]],
c(4.11501, NA, NA, 6.15413, NA, NA, 4.56183, NA, NA, 5.9878, NA,
NA, 4.4833, NA, NA, 3.67224, NA, NA, 5.20914, NA, NA, 3.69412,
NA, NA, 3.36803, NA, NA, 4.63535, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["df.error"]],
c(2, NA, NA, 9, NA, NA, 4, NA, NA, 3, NA, NA, 4, NA, NA, 11, NA,
NA, 7, NA, NA, 11, NA, NA, 9, NA, NA, 5, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["p.value"]],
c(0.05429, NA, NA, 0.00017, NA, NA, 0.01032, NA, NA, 0.00933, NA,
NA, 0.01096, NA, NA, 0.00368, NA, NA, 0.00124, NA, NA, 0.00354,
NA, NA, 0.00828, NA, NA, 0.00566, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["effect"]],
c("fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random", "fixed", "random", "random", "fixed", "random",
"random", "fixed", "random", "random", "fixed", "random", "random",
"fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random"),
fixed = TRUE)
expect_equal(
all_coeffs[["group"]],
c("", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual",
"", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_coeffs),
c("Repetition", "term", "estimate", "std.error", "conf.level", "conf.low",
"conf.high", "statistic", "df.error", "p.value", "effect", "group"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_coeffs),
c("integer", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "integer", "numeric", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_coeffs),
c("integer", "character", "double", "double", "double", "double",
"double", "double", "integer", "double", "character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_coeffs),
c(30L, 12L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_coeffs)),
character(0),
fixed = TRUE)
## Finished testing 'all_coeffs' ####
})
test_that("gaussian evaluations of random effects models are correct with REML FALSE in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline_random_effects <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
random_effects = "( 1 | session )",
n = 10,
family = "gaussian",
REML = FALSE,
metrics = "all",
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline_random_effects$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline_random_effects$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
19.9461643038004, 19.8243992085347, 3.16669902505845, 5.45212618245324,
24.498950920267, 15.8922827970791, 0, 0, 18.2579833481909
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
17.5688071767949, 17.5664123038964, 2.36581521783252, 3.32573044194396,
20.960459513901, 14.4129406656979, 0, 0, 16.3907116308631
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
80.3465725153843, 74.2525560821578, 29.4337002888526, 45.288244994372,
122.097050951734, 42.4261074987794, 0, 0, 152.799138513963
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2m,
rep(0.0, 9),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2c,
c(
0.43613229083168, 0.474672230635666, 0.149845112686991, 0.23376511982053,
0.624606985762511, 0.200718929950735, 0, 0, 0.397952267974043
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AICc,
c(
87.6265725153843, 80.2525560821578, 24.5752651300548, 39.9130171335876,
124.497050951734, 59.3905521226883, 0, 0, 154.513424228249
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
80.9234641973603, 74.6252989452318, 30.5156235782933, 46.8050859521017,
124.01422294058, 41.2544212360817, 0, 0, 155.470253787652
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
24.498950920267, 20.5418159984439, 18.992695846541, 23.6559983524106,
19.8835818741749, 15.8922827970791, 19.7652165428946, 16.7141367039409,
16.0496991056741, 23.4672648965776
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
20.960459513901, 18.4479900242695, 17.3968470787666, 20.6756807573846,
17.3014038386473, 14.4129406656979, 17.7359775290262, 15.1341822802114,
14.4362959391151, 19.1862941409292
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
42.4261074987794, 97.0575891839717, 65.3436873296684, 47.3905521226883,
57.2591370177335, 119.245364275572, 83.1614248346471, 122.097050951734,
106.339403446441, 63.1454084926068
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Random,
rep("(1|session)", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4042.804, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
## Testing 'all_coeffs' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_coeffs),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
all_coeffs[["Repetition"]],
c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
all_coeffs[["term"]],
c("(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)"),
fixed = TRUE)
expect_equal(
all_coeffs[["estimate"]],
c(29.04756, 4.78805, 8.17091, 34.32034, 6.2161, 9.52228, 41.24164,
7.6352, 15.23618, 29.94422, 5.55015, 5.98193, 31.78646, 8.81967,
6.83742, 37.66581, 13.50784, 11.12957, 34.3378, 7.2613, 9.92882,
35.96226, 12.45905, 12.79418, 36.2792, 13.87662, 12.74618, 27.11524,
7.22274, 6.58729),
tolerance = 1e-4)
expect_equal(
all_coeffs[["std.error"]],
c(5.00667887999703, 5.67371825188572, 3.32263054737512, 4.58125952668684,
4.19962069675716, 2.23902846895305, 7.4157534194802, 9.40523002404368,
5.22228218871595, 4.11850585776279, 3.92278892473836, 2.40582757012123,
5.86297959144672, 4.82362962007409, 2.40174657096886, 8.40640786916794,
6.41710541301027, 2.37215499333295, 5.47687326594097, 4.98399903152422,
2.61097972219224, 8.01516995843001, 6.33568334015868, 2.72644466961516,
8.84393766439164, 6.93470544419571, 3.00359441565683, 4.79166391324045,
3.95574719227331, 2.08166583997345),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.level"]],
c(0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.low"]],
c(7.5055635091569, 0.46936052606292, 3.68244920326646, 23.9568134523162,
1.65364927534353, 6.00610431660554, 20.6522095649089, 0.682793897696677,
7.78255751654273, 16.8372929040608, 1.38894433687881, 2.71959079455526,
15.5082176576566, 3.01936677556473, 3.43474719144677, 19.1634263807258,
5.3236524900518, 7.32915051912752, 21.3870490121703, 1.89133471569308,
5.93003670321845, 18.3209851742201, 4.59865828520289, 8.42599036674567,
16.2728226558001, 5.21079259307738, 8.03150734947051, 14.7978731028873,
2.46897286078753, 3.54581770261687),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.high"]],
c(50.5895646091533, 48.8439653196076, 18.1302694700696, 44.6838715581805,
23.366460023273, 15.0969356177559, 61.8310741306481, 85.3790886729535,
29.8284029460688, 43.0511404047138, 22.17813974328, 13.1576934421859,
48.0646996347502, 25.7625665742604, 13.6109854945425, 56.1681843201583,
34.2737942870217, 16.9006519912674, 47.2885437072587, 27.8779371562027,
16.6240922819864, 53.6035254424537, 33.7550247871271, 19.426912395414,
56.2855765254888, 36.9541620366811, 20.2284566698823, 39.4326015385872,
21.1294251635629, 12.2376231801648),
tolerance = 1e-4)
expect_equal(
all_coeffs[["statistic"]],
c(5.80176, NA, NA, 7.49146, NA, NA, 5.56136, NA, NA, 7.27065, NA,
NA, 5.42155, NA, NA, 4.48061, NA, NA, 6.2696, NA, NA, 4.48677,
NA, NA, 4.10215, NA, NA, 5.65884, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["df.error"]],
c(2, NA, NA, 9, NA, NA, 4, NA, NA, 3, NA, NA, 4, NA, NA, 11, NA,
NA, 7, NA, NA, 11, NA, NA, 9, NA, NA, 5, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["p.value"]],
c(0.02845, NA, NA, 4e-05, NA, NA, 0.00512, NA, NA, 0.00537, NA,
NA, 0.00561, NA, NA, 0.00093, NA, NA, 0.00042, NA, NA, 0.00092,
NA, NA, 0.00267, NA, NA, 0.0024, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["effect"]],
c("fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random", "fixed", "random", "random", "fixed", "random",
"random", "fixed", "random", "random", "fixed", "random", "random",
"fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random"),
fixed = TRUE)
expect_equal(
all_coeffs[["group"]],
c("", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual",
"", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_coeffs),
c("Repetition", "term", "estimate", "std.error", "conf.level", "conf.low",
"conf.high", "statistic", "df.error", "p.value", "effect", "group"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_coeffs),
c("integer", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "integer", "numeric", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_coeffs),
c("integer", "character", "double", "double", "double", "double",
"double", "double", "integer", "double", "character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_coeffs),
c(30L, 12L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_coeffs)),
character(0),
fixed = TRUE)
## Finished testing 'all_coeffs' ####
})
# TODO Create baseline test where both classes are 50% 50% , 100% 0%, 0% 100%, 30/70 etc.
# Do we get what we expect?
test_that("multinomial evaluations are correct in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(1)
targets_1 <- dplyr::sample_n(tibble::enframe(rep(1:3, each = 10), value = "targets_3"), 25) %>%
dplyr::select(-"name")
targets_2 <- dplyr::sample_n(tibble::enframe(rep(1:4, each = 10), value = "targets_4"), 25) %>%
dplyr::select(-"name")
targets_3 <- dplyr::sample_n(tibble::enframe(rep(1:5, each = 10), value = "targets_5"), 25) %>%
dplyr::select(-"name")
different_targets <- targets_1 %>%
dplyr::bind_cols(targets_2, targets_3)
multinom_baseline <- baseline(
test_data = different_targets,
dependent_col = "targets_3",
n = 10,
family = "multinomial",
metrics = list(
"Weighted Accuracy" = TRUE,
"Accuracy" = FALSE,
"Specificity" = FALSE,
"AUC" = TRUE
),
parallel = FALSE
)
multinom_baseline_summ <- multinom_baseline$summarized_metrics
multinom_baseline_class <- tidyr::unnest(multinom_baseline$summarized_class_level_results, "Results")
multinom_baseline_random_eval_summ <- multinom_baseline$random_evaluations
multinom_baseline_random_eval_class <- dplyr::bind_rows(multinom_baseline$random_evaluations$`Class Level Results`)
# Summarized results
if (TRUE) {
# # Summarized results
expect_equal(
colnames(multinom_baseline_summ),
c(
"Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Accuracy", "F1", "Sensitivity",
"Pos Pred Value", "Neg Pred Value", "AUC",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"
)
)
expect_equal(
multinom_baseline_summ$Measure,
c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "CL_Max", "CL_Min",
"CL_NAs", "CL_INFs", "All_1", "All_2", "All_3"
)
)
expect_equal(multinom_baseline_summ$`Balanced Accuracy`,
c(
0.567046957671958, 0.566798941798942, 0.0945059449777177, 0.118138227513228,
0.73776455026455, 0.427744708994709, 0, 0,
0.845238095238095, 0.349206349206349, 0, 0, 0.5, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted Accuracy`,
c(
0.61312, 0.616, 0.0844143852932925, 0.1224, 0.7536, 0.4976, 0, 0, NA, NA,
NA, NA, 0.5616, 0.5392, 0.5616
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$F1,
c(
0.414096213384139, 0.412686127082412, 0.126458068440281, 0.171577755033637,
0.638304093567251, 0.237745098039216, 0, 0,
0.75, 0.125, 0, 0, NA, NA, NA
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Sensitivity,
c(
0.422751322751323, 0.420634920634921, 0.126356719688141, 0.149470899470899,
0.656084656084656, 0.232804232804233, 0, 0,
0.857142857142857, 0.111111111111111, 0, 0, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Pos Pred Value`,
c(
0.421105098605099, 0.424404761904762, 0.130757940506255, 0.18994708994709,
0.644444444444444, 0.24537037037037, 0, 0,
0.714285714285714, 0.111111111111111, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Neg Pred Value`,
c(
0.712455474092471, 0.710389433551198, 0.0639629682811759, 0.0885480929888824,
0.824780701754386, 0.620098039215686, 0, 0,
0.9375, 0.538461538461539, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$AUC,
c(
0.557671957671958, 0.546443268665491, 0.107836177836045, 0.127572016460905,
0.766901822457378, 0.397119341563786, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Kappa,
c(
0.132055158549256, 0.134473657323828, 0.187086327373864, 0.243825884877481,
0.462128481918942, -0.1375345264999, 0, 0,
0.635036496350365, -0.277372262773723, 0, 0, 0, 0, 0
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$MCC,
c(0.135653399205696, 0.136605182450698, 0.190316058026005, 0.255168588966898,
0.469598884822668, -0.137349796341982, 0, 0, NA, NA, NA, NA,
0, 0, 0),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Detection Rate`,
c(
0.14, 0.14, 0.0422806613120899, 0.0566666666666667,
0.213333333333333, 0.08, 0, 0,
0.24, 0.04, 0, 0, 0.12, 0.0933333333333333, 0.12
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 3.70074341541719e-17,
5.55111512312578e-17, 0.333333333333333, 0.333333333333333, 0,
0, 0.48, 0.16, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Prevalence,
c(
0.333333333333333, 0.333333333333333, 0, 0, 0.333333333333333,
0.333333333333333, 0, 0, 0.36, 0.28, 0,
0, 0.333333333333333, 0.333333333333333, 0.333333333333333
),
tolerance = 1e-3
)
}
# Summarized class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_class),
c(
"Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Pos Pred Value", "Neg Pred Value", "Kappa", "Detection Rate",
"Detection Prevalence", "Prevalence"
)
)
expect_equal(
multinom_baseline_class$Class,
as.character(rep(1:3, each = 10))
)
expect_equal(
multinom_baseline_class$Measure,
rep(c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0", "All_1"), 3)
)
expect_equal(sum(multinom_baseline_class$`Balanced Accuracy`), 10.58557, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Accuracy, na.rm = TRUE), 11.01848, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$F1, na.rm = TRUE), 7.506455, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Sensitivity), 9.202117, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Pos Pred Value`, na.rm = T), 7.107812, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Neg Pred Value`, na.rm = T), 11.06547, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Kappa), 3.139021, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Rate`), 3.067856, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Prevalence`), 7.413783, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Prevalence), 6)
}
# Random evaluations results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_summ),
c(
"Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Accuracy",
"F1", "Sensitivity", "Pos Pred Value", "Neg Pred Value", "AUC",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "Predictions", "ROC", "Confusion Matrix", "Class Level Results",
"Process", "Dependent"
)
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[2]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$Predictions[[1]]),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
multinom_baseline_random_eval_summ$Repetition,
1:10
)
expect_equal(multinom_baseline_random_eval_summ$`Overall Accuracy`,
c(0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44, 0.40, 0.32, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Balanced Accuracy`,
c(
0.6651786, 0.5239749, 0.7377646, 0.4661045, 0.6362434,
0.4277447, 0.5806878, 0.5529101, 0.4980159, 0.5818452
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Accuracy`,
c(
0.7088, 0.5744, 0.7536, 0.5168, 0.68, 0.4976, 0.6288, 0.6032,
0.536, 0.632
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$F1,
c(
0.5511905, 0.3472767, 0.6383041, 0.2769608, 0.5111111,
0.2377451, 0.4398148, 0.4019608, 0.3131868, 0.4234115
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Sensitivity,
c(
0.5502646, 0.3650794, 0.6560847, 0.2910053, 0.5132275,
0.2328042, 0.4391534, 0.4021164, 0.3386243, 0.4391534
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Pos Pred Value`,
c(
0.5627706, 0.3407407, 0.6444444, 0.2726190, 0.5317460,
0.2453704, 0.4497354, 0.4154762, 0.3148148, 0.4333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Neg Pred Value`,
c(
0.7804233, 0.6874269, 0.8247807, 0.6453159, 0.7612836,
0.6200980, 0.7199074, 0.7008715, 0.6566697, 0.7277778
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$AUC,
c(
0.666960611405056, 0.529100529100529, 0.766901822457378, 0.496766607877719,
0.563786008230453, 0.397119341563786, 0.6222810111699, 0.481187536743092,
0.468841857730747, 0.583774250440917
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Kappa,
c(
0.33221743, 0.04113827, 0.46212848, -0.07364408,
0.27425438, -0.13753453, 0.16320289, 0.10883768,
-0.01015859, 0.16010964
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$MCC,
c(0.339040525810531, 0.0486630968728153, 0.469598884822668, -0.0726394381146181,
0.281551008833778, -0.137349796341982, 0.164251207729469, 0.108959157171927,
-0.0175562702416747, 0.172015615514047),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Rate`,
c(
0.18666667, 0.12000000, 0.21333333, 0.09333333,
0.17333333, 0.08000000, 0.14666667, 0.13333333,
0.10666667, 0.14666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Prevalence`,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Prevalence,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$Dependent,
rep("targets_3", 10)
)
expect_equal(
length(multinom_baseline_random_eval_summ$Predictions),
10
)
expect_equal(
length(multinom_baseline_random_eval_summ$`Confusion Matrix`),
10
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$Target,
as.character(c(1, 2, 2, 3, 1, 3, 3, 2, 2, 1, 1, 1, 2, 1, 2, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3))
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$`Predicted Class`,
c(
"2", "1", "2", "3", "1", "2", "3", "3", "2", "1", "3", "1",
"3", "3", "2", "2", "3", "3", "1", "3", "1", "3", "3", "1", "2"
)
)
preds <- dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions[[1]]$Prediction)
expect_equal(preds$`1`,
c(
0.3628243, 0.3609037, 0.2858221, 0.3747737, 0.3817009,
0.2737241, 0.3419418, 0.3049587, 0.2962721, 0.4240434,
0.2931166, 0.4357583, 0.2827270, 0.2248048, 0.1981635,
0.2229453, 0.3005942, 0.3278586, 0.4148558, 0.3384068,
0.4079048, 0.3290621, 0.3476237, 0.4361450, 0.3350177
),
tolerance = 1e-4
)
expect_equal(preds$`2`,
c(
0.3857806, 0.3426045, 0.4230804, 0.2251754, 0.3129899,
0.4752175, 0.2249170, 0.3047626, 0.4820548, 0.2148414,
0.3443212, 0.3331387, 0.2928041, 0.3148037, 0.4530263,
0.4719141, 0.3380788, 0.2966119, 0.2536526, 0.3053621,
0.2405719, 0.3336935, 0.2739201, 0.2804216, 0.3779097
),
tolerance = 1e-4
)
expect_equal(preds$`3`,
c(
0.2513951, 0.2964917, 0.2910974, 0.4000508, 0.3053092,
0.2510584, 0.4331413, 0.3902787, 0.2216731, 0.3611152,
0.3625622, 0.2311030, 0.4244689, 0.4603915, 0.3488102,
0.3051406, 0.3613270, 0.3755294, 0.3314917, 0.3562311,
0.3515233, 0.3372444, 0.3784562, 0.2834334, 0.2870727
),
tolerance = 1e-4
)
all_preds <- dplyr::bind_rows(
dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions)$Prediction
)
expect_equal(
all_preds$`1`,
c(
0.362824302965311, 0.360903717826462, 0.285822146515266, 0.37477371815703,
0.381700937144022, 0.273724077239358, 0.341941761553692, 0.304958692149421,
0.296272111439159, 0.42404336282095, 0.293116571086624, 0.435758286831625,
0.28272698563188, 0.224804797891925, 0.198163481343272, 0.222945305755905,
0.300594211265473, 0.3278586388128, 0.414855759724269, 0.33840681107911,
0.407904832070239, 0.329062099428566, 0.347623656119297, 0.436145034077774,
0.335017655729982, 0.331364482540279, 0.283536882101457, 0.281143446843595,
0.425995399790343, 0.261537001684637, 0.237313955938272, 0.23261151589263,
0.385748271928449, 0.334411930525475, 0.401249217427923, 0.420235849517611,
0.425289710559998, 0.262465444718002, 0.336697104880243, 0.231858151646129,
0.210092588546093, 0.47741912516683, 0.244565586519055, 0.286947978160956,
0.417209987822813, 0.409276670773936, 0.354307298336307, 0.276564090289706,
0.330862775865476, 0.44695225847507, 0.317576338796576, 0.382847300413345,
0.224081474342672, 0.308365835600141, 0.302015399930661, 0.313332459936172,
0.251934064646274, 0.23915184388103, 0.277538091890923, 0.471891254622384,
0.40754972988395, 0.345372198209239, 0.33466402327811, 0.462461145964111,
0.266579885476856, 0.352899631920573, 0.421312945065338, 0.200478726356666,
0.309794998292503, 0.380501567619241, 0.370761060364264, 0.286594888362904,
0.361067815387182, 0.245947918320429, 0.389260130880553, 0.321714471097802,
0.331089372813525, 0.256210536793289, 0.285625126260833, 0.27568485403864,
0.274822151212381, 0.389484563107126, 0.212979233666992, 0.296561920614094,
0.200225006332582, 0.242554965054235, 0.401234678799669, 0.330009524801572,
0.348663184860405, 0.411214822720279, 0.307151655834041, 0.204995757155556,
0.351402327921561, 0.374094844465352, 0.248711786514304, 0.336708510840726,
0.408292124109811, 0.357562258085327, 0.34181138284562, 0.323252856286123,
0.447544862966328, 0.429915527750429, 0.363601070683015, 0.333552425710284,
0.398225671426714, 0.221696833964976, 0.271265251552504, 0.481294916218702,
0.328149707350169, 0.450855293083027, 0.237104558776676, 0.374383211394644,
0.443948913175648, 0.494051191743219, 0.256273986260961, 0.489484795353784,
0.329464982964633, 0.220032486448317, 0.408557119092641, 0.319230850919545,
0.355237403525459, 0.23455484947749, 0.382020061725786, 0.267110221515588,
0.449602783872994, 0.202164855975727, 0.236445026776993, 0.319748604954957,
0.293320401651886, 0.287711207487059, 0.358419477510523, 0.346556723204159,
0.206074092549024, 0.22717751457188, 0.397646700390622, 0.446688182141175,
0.25620775941098, 0.501804532497241, 0.408063047760274, 0.443251289188255,
0.249444482951462, 0.383829461406706, 0.526313287884649, 0.452849242524878,
0.253013631607335, 0.334191437619488, 0.210413611589952, 0.23421023559276,
0.321507174883409, 0.509517153778936, 0.315671789411075, 0.255620649439559,
0.239331881508991, 0.322754669547894, 0.435042212296461, 0.251412865189562,
0.282387104005381, 0.245973141871465, 0.341257410449456, 0.277527088361948,
0.399220690874693, 0.439926753580167, 0.345819178673885, 0.305382501740128,
0.418275547531307, 0.357628745014896, 0.294197703135495, 0.438696916274104,
0.316517159370203, 0.357792733303972, 0.28831445694109, 0.327860691995961,
0.22281288859293, 0.314065020723387, 0.459321941470143, 0.387693577991577,
0.363268586267414, 0.303950826699894, 0.372655080723904, 0.339529039157154,
0.224621024989129, 0.296482850978123, 0.314332611351849, 0.234267899937577,
0.437859055024521, 0.269731707168111, 0.331690324355338, 0.280473038011755,
0.399824753263675, 0.383719422449624, 0.330850303521819, 0.225277208971775,
0.359553396121816, 0.328615974470287, 0.231945476475709, 0.214223736176299,
0.20582242433687, 0.375727645283227, 0.235524783117192, 0.286268463291008,
0.229673192858615, 0.283772599798294, 0.219611172339202, 0.295826542146669,
0.285141592656138, 0.192558055771125, 0.355721326100929, 0.409545410065343,
0.331835694337497, 0.230954644322117, 0.340582165139984, 0.294251623668547,
0.298768624646445, 0.291985965689547, 0.300172335564108, 0.318523203157749,
0.237566842343213, 0.37261470439223, 0.331390765035546, 0.364342409833174,
0.397916841690445, 0.217399738068907, 0.264193767365295, 0.208427577921687,
0.420006306243977, 0.292308232373888, 0.281443833338, 0.30947189334135,
0.257203806304276, 0.295150877610198, 0.439630183471502, 0.321730635029117,
0.383315062595601, 0.225583239939362, 0.309872283286329, 0.255887220229597,
0.316939046835727, 0.449868348167623, 0.304614105760487, 0.248742717566446,
0.305293159179038, 0.222558427746317, 0.244342581208601, 0.503534783305527,
0.286383868562386, 0.428804177911175, 0.295404759872436, 0.30687255200497,
0.445596166797302, 0.334706962656109
)
)
expect_equal(
all_preds$`2`,
c(
0.385780580440307, 0.342604539124775, 0.423080439134247, 0.225175445397002,
0.312989908016585, 0.475217543730522, 0.224916968457944, 0.30476258690942,
0.482054751251862, 0.214841434476723, 0.344321198446437, 0.333138708845406,
0.292804142075078, 0.31480368331023, 0.453026287138725, 0.4719140606185,
0.338078757628025, 0.296611945042962, 0.25365258932653, 0.305362051955673,
0.240571861737913, 0.333693522274202, 0.27392009695986, 0.28042156343828,
0.377909676645575, 0.279959262495121, 0.2122887561223, 0.470196696415608,
0.239862567097843, 0.370221172919109, 0.25443866213747, 0.477173210501813,
0.259642635838842, 0.322066433258374, 0.353453695410528, 0.1765751153703,
0.285504011914052, 0.424456315084771, 0.288460995364288, 0.35958455461334,
0.478761722406255, 0.250631249168976, 0.445446891169545, 0.369385615626211,
0.349831286201674, 0.234976115878086, 0.378701320138235, 0.431201840261207,
0.350278234492975, 0.262288382369503, 0.307058606700166, 0.320533642024935,
0.43236586844692, 0.292897655407014, 0.272621596850078, 0.270996516799443,
0.410085989322878, 0.444842557489891, 0.388276130257391, 0.235166674305724,
0.285112014266039, 0.437273128065374, 0.454306056457757, 0.193441477963145,
0.420457813840421, 0.20658635492773, 0.266523286854865, 0.398245797218399,
0.397335280211415, 0.214226033921459, 0.334049449261309, 0.465337959942035,
0.233723758733514, 0.31472699114585, 0.190099685465158, 0.336264164127875,
0.228217420461845, 0.478951976081042, 0.445589110407979, 0.429453925922651,
0.472930553187392, 0.332138049773757, 0.431357175765288, 0.227158863320801,
0.414490874557254, 0.492841238109576, 0.234359938505408, 0.446090237254051,
0.35319859961771, 0.345280017404155, 0.327026905651897, 0.315397998189469,
0.341892635859671, 0.363505947910766, 0.404023366558927, 0.276862535515932,
0.294742179813213, 0.294768056642531, 0.266684517736902, 0.311820705852887,
0.274265040914617, 0.245816171241433, 0.440341510685916, 0.421104352741149,
0.388918784994157, 0.421278084276545, 0.240879703622285, 0.289683431392311,
0.339677067961078, 0.208333263474704, 0.437464387830416, 0.367535015967519,
0.226639265821188, 0.311401754535334, 0.372083534671264, 0.250419612561257,
0.299632515128755, 0.32824246642957, 0.297400161614639, 0.313227906367457,
0.300459136297168, 0.303048382688999, 0.423041978939619, 0.21995462356869,
0.220422428036373, 0.405812939731575, 0.295769536850216, 0.276168343895395,
0.265750665287833, 0.253402549071951, 0.411972235353439, 0.351927605924981,
0.485986422454677, 0.321551112338582, 0.206970975070247, 0.200850312668311,
0.372875934652805, 0.234374628487236, 0.245897425556106, 0.253770902739585,
0.468416469360073, 0.410120970528605, 0.238800068178443, 0.280904468157008,
0.359143089366176, 0.407538360613741, 0.463482198596469, 0.346052750154407,
0.287160871624961, 0.227514884062728, 0.240774210253124, 0.468578579358505,
0.353317373812137, 0.349640083059674, 0.369950508544139, 0.380002971753307,
0.289234489357681, 0.395516529035609, 0.467797098876064, 0.338410210733615,
0.331006277488809, 0.266926876246406, 0.198909961692134, 0.477956034101944,
0.304153252664601, 0.269291217911081, 0.42201242766336, 0.236603843824688,
0.323467921547914, 0.352132946072107, 0.369703528563403, 0.329596532065509,
0.327226373739197, 0.462085224932779, 0.189039991684081, 0.314951443633383,
0.284435887439821, 0.366590341963145, 0.395601714171088, 0.335225687954128,
0.5311988845767, 0.346007208388757, 0.446522267443117, 0.343863071903928,
0.292892871402239, 0.382830131827548, 0.385357786983514, 0.214142145338179,
0.376626067248013, 0.389178684389126, 0.338344548396213, 0.403001958525644,
0.290654812925342, 0.247101836432825, 0.370492901531161, 0.461281265139314,
0.375012631382266, 0.363470643270294, 0.226067381809261, 0.313047833074042,
0.479443190691509, 0.490484639496364, 0.391240933517263, 0.478047191943444,
0.356438854507902, 0.343453444225452, 0.216202187035667, 0.237739153185802,
0.301640124052444, 0.247512173039711, 0.309589249125308, 0.308972701838745,
0.358423819908425, 0.304335319964878, 0.448013834885817, 0.378099789829676,
0.541697788505534, 0.268360413749574, 0.331964563898707, 0.26728121083406,
0.225911392328278, 0.533006780442077, 0.342769074491857, 0.347630476342447,
0.345065351570056, 0.35598982747141, 0.453613405495365, 0.191197557332096,
0.401269074847496, 0.438308839883431, 0.239209391932476, 0.29631107556132,
0.178586958336824, 0.478858385133731, 0.475817691964852, 0.427658658905656,
0.298338105256703, 0.272983994354953, 0.308124320381545, 0.471773233423636,
0.373331904052012, 0.260774223314073, 0.326618966011962, 0.252950597054979,
0.399516649309411, 0.258469593402028, 0.328387994212459, 0.263157498352965,
0.180860493953652, 0.325672027376522
)
)
expect_equal(
all_preds$`3`,
c(
0.251395116594382, 0.296491743048763, 0.291097414350488, 0.400050836445967,
0.305309154839393, 0.25105837903012, 0.433141269988364, 0.390278720941159,
0.221673137308979, 0.361115202702327, 0.362562230466938, 0.231103004322969,
0.424468872293042, 0.460391518797845, 0.348810231518003, 0.305140633625595,
0.361327031106502, 0.375529416144238, 0.331491650949201, 0.356231136965217,
0.351523306191848, 0.337244378297231, 0.378456246920842, 0.283433402483946,
0.287072667624444, 0.3886762549646, 0.504174361776243, 0.248659856740797,
0.334142033111814, 0.368241825396254, 0.508247381924258, 0.290215273605557,
0.354609092232709, 0.343521636216152, 0.245297087161549, 0.403189035112089,
0.28920627752595, 0.313078240197227, 0.374841899755469, 0.408557293740532,
0.311145689047652, 0.271949625664194, 0.3099875223114, 0.343666406212833,
0.232958725975514, 0.355747213347978, 0.266991381525458, 0.292234069449087,
0.318858989641549, 0.290759359155428, 0.375365054503257, 0.29661905756172,
0.343552657210408, 0.398736508992845, 0.425363003219261, 0.415671023264385,
0.337979946030848, 0.316005598629079, 0.334185777851686, 0.292942071071892,
0.30733825585001, 0.217354673725387, 0.211029920264134, 0.344097376072744,
0.312962300682723, 0.440514013151697, 0.312163768079797, 0.401275476424934,
0.292869721496081, 0.4052723984593, 0.295189490374428, 0.24806715169506,
0.405208425879304, 0.439325090533721, 0.420640183654289, 0.342021364774323,
0.440693206724629, 0.264837487125669, 0.268785763331188, 0.294861220038709,
0.252247295600227, 0.278377387119117, 0.35566359056772, 0.476279216065105,
0.385284119110164, 0.264603796836189, 0.364405382694923, 0.223900237944377,
0.298138215521885, 0.243505159875566, 0.365821438514061, 0.479606244654974,
0.306705036218769, 0.262399207623883, 0.347264846926769, 0.386428953643342,
0.296965696076976, 0.347669685272142, 0.391504099417478, 0.364926437860989,
0.278190096119055, 0.324268301008138, 0.196057418631069, 0.245343221548567,
0.212855543579129, 0.357025081758479, 0.487855044825211, 0.229021652388988,
0.332173224688753, 0.340811443442268, 0.325431053392908, 0.258081772637838,
0.329411821003164, 0.194547053721447, 0.371642479067775, 0.260095592084959,
0.370902501906612, 0.451725047122112, 0.29404271929272, 0.367541242712998,
0.344303460177374, 0.462396767833511, 0.194937959334594, 0.512935154915721,
0.329974788090632, 0.392022204292698, 0.467785436372791, 0.404083051149648,
0.440928933060281, 0.45888624344099, 0.229608287136038, 0.30151567087086,
0.307939484996299, 0.451271373089538, 0.395382324539131, 0.352461505190514,
0.370916305936214, 0.263820839015524, 0.34603952668362, 0.30297780807216,
0.282139047688465, 0.206049568064689, 0.234886643936908, 0.266246289318114,
0.387843279026488, 0.258270201766771, 0.326104189813578, 0.419737014252832,
0.39133195349163, 0.262967962158336, 0.443554000335801, 0.275800771201936,
0.407350744678872, 0.327605247392432, 0.1950072791594, 0.36858416305713,
0.428378406636938, 0.358510329092926, 0.190945490674481, 0.384062700904437,
0.269773031636498, 0.293146370173427, 0.455270859633981, 0.216661464157928,
0.277571199804092, 0.373080037074023, 0.283789869201145, 0.324699239901208,
0.360014919081883, 0.290074320623921, 0.341982014495507, 0.34254277593853,
0.449960737667873, 0.223849754343834, 0.351638066845776, 0.29735497837504,
0.352295526292765, 0.329458831336961, 0.231743205105008, 0.325245272888717,
0.24418009043417, 0.35750994063312, 0.239145121205034, 0.421869028158495,
0.269248073573241, 0.347438161004341, 0.282951888661148, 0.505384816650066,
0.223549179488312, 0.22710189316125, 0.330805148081968, 0.371720832502581,
0.349791790952842, 0.424282189096888, 0.39756162199313, 0.324494998684386,
0.419164944280864, 0.260801711446479, 0.538407835073546, 0.400683703634949,
0.290883616449876, 0.225742760705341, 0.389147894143535, 0.226126265909887,
0.35841955283596, 0.463988500003423, 0.428076486863404, 0.352715436748856,
0.366524181610059, 0.521533182638172, 0.349828585734708, 0.396775674492708,
0.34280755544513, 0.403678714345574, 0.251813829550075, 0.303377007012575,
0.220735369151253, 0.359024881858196, 0.336644671065747, 0.368376379332766,
0.376171765981278, 0.249593481489016, 0.393037158142848, 0.443941945735865,
0.234928342185967, 0.351701940154702, 0.264942761166635, 0.499330549326554,
0.341527118848228, 0.266540282506371, 0.321160424596023, 0.381958289409563,
0.438097979067575, 0.295558374926908, 0.214310024748819, 0.316454120864747,
0.38472284790757, 0.277147657477424, 0.387261573857968, 0.279484049009918,
0.32137493676895, 0.51666734893961, 0.429038452779437, 0.243514619639494,
0.314099482128203, 0.312726228686797, 0.376207245915106, 0.429969949642065,
0.373543339249046, 0.339621009967369
)
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Prediction,
c("1", "2", "3", "1", "2", "3", "1", "2", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Target,
c("1", "1", "1", "2", "2", "2", "3", "3", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$N,
c(5, 1, 3, 2, 3, 2, 0, 3, 6)
)
# Test first 'Process'
## Testing 'multinom_baseline_random_eval_summ$Process...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_10358 <- multinom_baseline_random_eval_summ$Process[[1]]
# Testing class
expect_equal(
class(output_10358),
"process_info_multinomial",
fixed = TRUE)
# Testing type
expect_type(
output_10358,
type = "list")
# Testing names
expect_equal(
names(output_10358),
c("Target Column", "Prediction Columns", "ID Column", "Family",
"Classes", "Softmax Applied", "Target Summary", "Prediction Summary",
"Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_10358$`Target Column`,
"targets_3"
)
expect_equal(
output_10358$`Prediction Columns`,
c("1","2", "3")
)
expect_equal(
output_10358$`ID Column`,
NULL
)
expect_equal(
output_10358$Family,
"Multinomial"
)
expect_equal(
output_10358$Classes,
c("1", "2", "3")
)
expect_equal(
output_10358$`Softmax Applied`,
FALSE
)
expect_equal(
output_10358$`Target Summary`,
list(Total = 25L,
`Class Counts` = c(`1` = 9, `2` = 7, `3` = 9))
)
expect_equal(
output_10358$`Prediction Summary`,
list(Total = 25L,
`Class Counts` = c(`1` = 7, `2` = 7, `3` = 11))
)
expect_true(
output_10358$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'multinom_baseline_random_eval_summ$Process...' ####
## Testing 'as.character(multinom_baseline_random_eval...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(multinom_baseline_random_eval_summ$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: targets_3\nPr",
"ediction columns: 1, 2, 3\nFamily / type: Multinomial\nClass",
"es: 1, 2, 3\nSoftmax: Not applied\nTarget counts: total=25, ",
"1=9, 2=7, 3=9\nPrediction counts: total=25, 1=7, 2=7, 3=11\n",
"Locale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(multinom_baseline_random_eval...' ####
}
# Random evaluations class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_class),
c(
"Repetition", "Class", "Balanced Accuracy", "Accuracy", "F1",
"Sensitivity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence",
"Prevalence", "Support", "Confusion Matrix", "Family",
"Dependent"
)
)
expect_equal(
length(multinom_baseline_random_eval_class$`Confusion Matrix`),
30
)
expect_equal(
colnames(multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]),
c("Class", "Prediction", "Target", "Pos_0", "Pos_1", "N")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Prediction,
c("0", "1", "0", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Target,
c("0", "0", "1", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_0,
c("TP", "FN", "FP", "TN")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_1,
c("TN", "FP", "FN", "TP")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$N,
c(14, 2, 4, 5)
)
expect_equal(
multinom_baseline_random_eval_class$Repetition,
rep(1:10, each = 3)
)
expect_equal(
multinom_baseline_random_eval_class$Class,
as.character(rep(1:3, 10))
)
expect_equal(sum(multinom_baseline_random_eval_class$`Balanced Accuracy`),
17.01141,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Accuracy),
18.4,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$F1),
12.42289,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Sensitivity),
12.68254,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Pos Pred Value`),
12.63315,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Neg Pred Value`),
21.37366,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Kappa),
3.961655,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Rate`),
4.2,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Prevalence`),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Prevalence),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Support),
250,
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Family,
rep("binomial", 30),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Dependent,
rep("targets_3", 30),
tolerance = 1e-4
)
}
## Custom random generator fn
# Multinomial with custom random generator function
# that creates very "certain" predictions
# (once softmax is applied)
rcertain <- function(n) {
(runif(n, min = 1, max = 100)^1.4) / 100
}
xpectr::set_test_seed(1)
multinom_baseline_certain <- baseline(
test_data = different_targets,
dependent_col = "targets_3",
n = 10, family = "multinomial",
metrics = "all",
random_generator_fn = rcertain
)
expect_equal(
names(multinom_baseline_certain),
c(
"summarized_metrics", "summarized_class_level_results",
"random_evaluations"
)
)
multinom_baseline_summ <- multinom_baseline_certain$summarized_metrics
multinom_baseline_class <- tidyr::unnest(multinom_baseline_certain$summarized_class_level_results, "Results")
multinom_baseline_random_eval_summ <- multinom_baseline_certain$random_evaluations
multinom_baseline_random_eval_class <- dplyr::bind_rows(multinom_baseline_certain$random_evaluations$`Class Level Results`)
# Summarized results
if (TRUE) {
# # Summarized results
expect_equal(
colnames(multinom_baseline_summ),
c(
"Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate",
"Detection Prevalence", "Weighted Detection Prevalence", "Prevalence",
"Weighted Prevalence", "False Neg Rate", "Weighted False Neg Rate",
"False Pos Rate", "Weighted False Pos Rate", "False Discovery Rate",
"Weighted False Discovery Rate", "False Omission Rate", "Weighted False Omission Rate",
"Threat Score", "Weighted Threat Score"
)
)
expect_equal(
multinom_baseline_summ$Measure,
c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "CL_Max",
"CL_Min", "CL_NAs", "CL_INFs", "All_1", "All_2", "All_3"
)
)
expect_equal(multinom_baseline_summ$`Overall Accuracy`,
c(
0.416, 0.4, 0.138820427571417, 0.23, 0.64, 0.24, 0, 0, NA, NA, NA, NA, 0.36,
0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Balanced Accuracy`,
c(
0.562285052910053, 0.552331349206349, 0.102909289295557, 0.16025958994709,
0.73776455026455, 0.427744708994709, 0, 0,
0.845238095238095, 0.349206349206349, 0, 0, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$F1,
c(
0.410443406496038, 0.393545751633987, 0.135473485139399, 0.213743453255837,
0.638304093567251, 0.237745098039216, 0, 0,
0.75, 0.125, 0, 0, NA, NA, NA
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Sensitivity,
c(
0.416931216931217, 0.402116402116402, 0.137153997558857, 0.207671957671958,
0.656084656084656, 0.232804232804233, 0, 0,
0.857142857142857, 0.111111111111111, 0, 0, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Specificity,
c(
0.707638888888889, 0.702546296296296, 0.0688421238509424, 0.112847222222222,
0.819444444444444, 0.622685185185185, 0, 0,
0.888888888888889, 0.5, 0, 0, 0.666666666666667, 0.666666666666667,
0.666666666666667
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Pos Pred Value`,
c(
0.416391293891294, 0.395238095238095, 0.141112097555744, 0.232296176046176,
0.644444444444444, 0.24537037037037, 0, 0,
0.714285714285714, 0.111111111111111, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Neg Pred Value`,
c(
0.709738237625235, 0.703667153996101, 0.0702194379364757, 0.112253271754046,
0.824780701754386, 0.620098039215686, 0, 0,
0.9375, 0.533333333333333, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$AUC,
c(
0.562375073486185, 0.550117577895356, 0.124339882882094, 0.158877131099353,
0.789535567313345, 0.387125220458554, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Kappa,
c(
0.122510558981315, 0.102170581409235, 0.204023225460463, 0.328600213517171,
0.462128481918942, -0.1375345264999, 0, 0,
0.635036496350365, -0.277372262773723, 0, 0, 0, 0, 0
),
tolerance = 1e-3
)
expect_equal(
multinom_baseline_summ$MCC,
c(0.125850827588575, 0.106457152301142, 0.208258245757672, 0.337236969494147,
0.469598884822668, -0.137349796341982, 0, 0, NA, NA, NA, NA,
0, 0, 0),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Detection Rate`,
c(
0.138666666666667, 0.133333333333333, 0.0462734758571391, 0.0766666666666667,
0.213333333333333, 0.08, 0, 0,
0.28, 0.04, 0, 0, 0.12, 0.0933333333333333, 0.12
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 3.70074341541719e-17,
5.55111512312578e-17, 0.333333333333333, 0.333333333333333, 0,
0, 0.48, 0.16, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Prevalence,
c(
0.333333333333333, 0.333333333333333, 0, 0, 0.333333333333333,
0.333333333333333, 0, 0, 0.36, 0.28, 0,
0, 0.333333333333333, 0.333333333333333, 0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Accuracy,
c(
0.610666666666667, 0.6, 0.0925469517142782, 0.153333333333333,
0.76, 0.493333333333333, 0, 0,
0.84, 0.4, 0, 0, 0.573333333333333, 0.52, 0.573333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Neg Rate`,
c(
0.583068783068783, 0.597883597883598, 0.137153997558857, 0.207671957671958,
0.767195767195767, 0.343915343915344, 0, 0,
0.888888888888889, 0.142857142857143, 0, 0, 0.666666666666667,
0.666666666666667, 0.666666666666667
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Pos Rate`,
c(
0.292361111111111, 0.297453703703704, 0.0688421238509424, 0.112847222222222,
0.377314814814815, 0.180555555555556, 0, 0,
0.5, 0.111111111111111, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Discovery Rate`,
c(
0.583608706108706, 0.604761904761905, 0.141112097555744, 0.232296176046176,
0.75462962962963, 0.355555555555556, 0, 0,
0.888888888888889, 0.285714285714286, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Omission Rate`,
c(
0.290261762374765, 0.296332846003899, 0.0702194379364757, 0.112253271754046,
0.379901960784314, 0.175219298245614, 0, 0,
0.466666666666667, 0.0625, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(
multinom_baseline_summ$`Threat Score`,
c(0.272473109678992, 0.253357753357753, 0.111848069715885, 0.169954378410261,
0.475058275058275, 0.138095238095238, 0, 0, 0.6, 0.0666666666666667,
0, 0, 0.12, 0.0933333333333333, 0.12),
tolerance = 1e-5
)
# Weighted
expect_equal(multinom_baseline_summ$`Weighted Balanced Accuracy`,
c(
0.561458333333333, 0.553819444444444, 0.103263185776414, 0.169270833333333,
0.729166666666667, 0.434027777777778, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted F1`,
c(
0.411579872140244, 0.395911764705882, 0.137025880250425, 0.224998773672148,
0.629368421052632, 0.246764705882353, 0, 0, NA, NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Sensitivity`,
c(
0.416, 0.4, 0.138820427571417, 0.23, 0.64, 0.24, 0, 0, NA,
NA, NA, NA, 0.36,
0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Specificity`,
c(
0.706916666666667, 0.707638888888889, 0.0680290890839442, 0.108541666666667,
0.818333333333333, 0.628055555555556, 0, 0, NA,
NA, NA, NA, 0.64, 0.72, 0.64
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Pos Pred Value`,
c(
0.419315295815296, 0.40152380952381, 0.142644759250397, 0.24119733044733,
0.642666666666667, 0.256111111111111, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Neg Pred Value`,
c(
0.70546650298446, 0.700627192982456, 0.0712309833412443, 0.120213692224528,
0.815763157894737, 0.619705882352941, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Kappa`,
c(
0.12105498715267, 0.105437069515008, 0.205614210088936, 0.345995736389319,
0.448295840764429, -0.126347507597995, 0, 0, NA,
NA, NA, NA, 0, 0, 0
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Detection Rate`,
c(
0.14016, 0.1344, 0.0476443117742763, 0.0836, 0.2112, 0.0832,
0, 0, NA, NA, NA, NA, 0.1296, 0.0784, 0.1296
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Detection Prevalence`,
c(
0.33408, 0.3312, 0.00592936196679991, 0.00639999999999996,
0.3472, 0.328, 0, 0, NA,
NA, NA, NA, 0.36, 0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Prevalence`,
c(
0.3376, 0.3376, 0, 0, 0.3376, 0.3376, 0, 0, NA,
NA, NA, NA, 0.3376, 0.3376, 0.3376
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted Accuracy`,
c(
0.60864, 0.6016, 0.0927576915049816, 0.1576, 0.7536, 0.4976,
0, 0, NA, NA, NA, NA, 0.5616, 0.5392, 0.5616
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Neg Rate`,
c(
0.584, 0.6, 0.138820427571417, 0.23, 0.76, 0.36, 0, 0, NA, NA, NA, NA, 0.64,
0.72, 0.64
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Pos Rate`,
c(
0.293083333333333, 0.292361111111111, 0.0680290890839442, 0.108541666666667,
0.371944444444444, 0.181666666666667, 0, 0, NA,
NA, NA, NA, 0.36, 0.28, 0.36
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Discovery Rate`,
c(
0.580684704184704, 0.59847619047619, 0.142644759250396, 0.24119733044733,
0.743888888888889, 0.357333333333333, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Omission Rate`,
c(
0.29453349701554, 0.299372807017544, 0.0712309833412443, 0.120213692224528,
0.380294117647059, 0.184236842105263, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(
multinom_baseline_summ$`Weighted Threat Score`,
c(0.273521485703839, 0.255824175824176, 0.112601755397812, 0.179807138939492,
0.465062937062937, 0.143809523809524, 0, 0, NA, NA, NA, NA, 0.1296,
0.0784, 0.1296),
tolerance = 1e-5
)
}
# Summarized class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_class),
c(
"Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate",
"False Discovery Rate", "False Omission Rate", "Threat Score"
)
)
expect_equal(
multinom_baseline_class$Class,
as.character(rep(1:3, each = 10))
)
expect_equal(
multinom_baseline_class$Measure,
rep(c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0", "All_1"), 3)
)
expect_equal(sum(multinom_baseline_class$`Balanced Accuracy`), 10.68149, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Accuracy), 11.08082, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$F1, na.rm = TRUE), 7.599136, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Sensitivity), 9.372747, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Specificity), 12.17256, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Pos Pred Value`, na.rm = T), 7.221436, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Neg Pred Value`, na.rm = T), 11.1646, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Kappa), 3.323803, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Rate`), 3.121808, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Prevalence`), 7.440557, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Prevalence), 6)
expect_equal(sum(multinom_baseline_class$`False Neg Rate`), 11.01402, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Pos Rate`), 7.155891, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Discovery Rate`, na.rm = TRUE), 10.19412, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Omission Rate`, na.rm = TRUE), 4.99052, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Threat Score`), 5.3493824364086, tolerance = 1e-4)
}
# Random evaluations results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_summ),
c(
"Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate",
"Detection Prevalence", "Weighted Detection Prevalence", "Prevalence",
"Weighted Prevalence", "False Neg Rate", "Weighted False Neg Rate",
"False Pos Rate", "Weighted False Pos Rate", "False Discovery Rate",
"Weighted False Discovery Rate", "False Omission Rate", "Weighted False Omission Rate",
"Threat Score", "Weighted Threat Score", "Predictions", "ROC",
"Confusion Matrix", "Class Level Results", "Process", "Dependent"
)
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[2]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$Predictions[[1]]),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
multinom_baseline_random_eval_summ$Repetition,
1:10
)
expect_equal(multinom_baseline_random_eval_summ$`Overall Accuracy`,
c(0.28, 0.52, 0.32, 0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Balanced Accuracy`,
c(
0.458498677248677, 0.628637566137566, 0.498015873015873, 0.665178571428571,
0.523974867724868, 0.73776455026455, 0.466104497354497, 0.636243386243386,
0.427744708994709, 0.580687830687831
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Balanced Accuracy`,
c(
0.454861111111111, 0.631944444444444, 0.486111111111111, 0.670138888888889,
0.524305555555556, 0.729166666666667, 0.461805555555556, 0.638888888888889,
0.434027777777778, 0.583333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Accuracy,
c(
0.52, 0.68, 0.546666666666667, 0.706666666666667, 0.573333333333333,
0.76, 0.52, 0.68, 0.493333333333333, 0.626666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Accuracy`,
c(
0.5136, 0.6768, 0.536, 0.7088, 0.5744, 0.7536, 0.5168, 0.68,
0.4976, 0.6288
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$F1,
c(
0.283060592658116, 0.494896331738437, 0.324074074074074, 0.551190476190476,
0.347276688453159, 0.638304093567251, 0.276960784313726, 0.511111111111111,
0.237745098039216, 0.439814814814815
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted F1`,
c(
0.282848297213622, 0.505397129186603, 0.31, 0.561, 0.346823529411765,
0.629368421052632, 0.270882352941176, 0.517714285714286, 0.246764705882353,
0.445
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Sensitivity,
c(
0.28042328042328, 0.502645502645503, 0.338624338624339, 0.55026455026455,
0.365079365079365, 0.656084656084656, 0.291005291005291, 0.513227513227513,
0.232804232804233, 0.439153439153439
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Sensitivity`,
c(0.28, 0.52, 0.32, 0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Specificity,
c(
0.636574074074074, 0.75462962962963, 0.657407407407407, 0.780092592592593,
0.68287037037037, 0.819444444444444, 0.641203703703704, 0.759259259259259,
0.622685185185185, 0.722222222222222
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Specificity`,
c(
0.629722222222222, 0.743888888888889, 0.652222222222222, 0.780277777777778,
0.688611111111111, 0.818333333333333, 0.643611111111111, 0.757777777777778,
0.628055555555556, 0.726666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Pos Pred Value`,
c(
0.286904761904762, 0.512121212121212, 0.317460317460317, 0.562770562770563,
0.340740740740741, 0.644444444444444, 0.272619047619048, 0.531746031746032,
0.24537037037037, 0.44973544973545
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Pos Pred Value`,
c(
0.287, 0.513090909090909, 0.307301587301587, 0.573506493506494,
0.344, 0.642666666666667, 0.270428571428571, 0.54, 0.256111111111111,
0.459047619047619
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Neg Pred Value`,
c(
0.634204793028322, 0.761904761904762, 0.662037037037037, 0.78042328042328,
0.687426900584795, 0.824780701754386, 0.645315904139434, 0.761283550757235,
0.620098039215686, 0.719907407407407
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Neg Pred Value`,
c(
0.62716339869281, 0.761904761904762, 0.65, 0.780634920634921,
0.683754385964912, 0.815763157894737, 0.638274509803922, 0.759964012595592,
0.619705882352941, 0.7175
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$AUC,
c(
0.413286302175191, 0.663139329805996, 0.494415049970605, 0.668430335097002,
0.535273368606702, 0.789535567313345, 0.491769547325103, 0.564961787184009,
0.387125220458554, 0.615814226925338
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Kappa,
c(
-0.0806325398580661, 0.257340370152257, -0.0133650986753176,
0.332217431748538, 0.0411382701447751, 0.462128481918942, -0.0736440767694057,
0.274254384977631, -0.1375345264999, 0.163202892673696
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Kappa`,
c(
-0.087718063681632, 0.261854540403706, -0.0360401459854014,
0.342286889780485, 0.0416223142124975, 0.448295840764429, -0.0823426204548178,
0.279686799267905, -0.126347507597995, 0.169251824817518
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$MCC,
c(-0.0871673257375417, 0.269468324571013, -0.0169082125603865,
0.339040525810531, 0.0486630968728153, 0.469598884822668, -0.0726394381146181,
0.281551008833778, -0.137349796341982, 0.164251207729469),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Rate`,
c(
0.0933333333333333, 0.173333333333333, 0.106666666666667, 0.186666666666667,
0.12, 0.213333333333333, 0.0933333333333333, 0.173333333333333,
0.08, 0.146666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Detection Rate`,
c(
0.0944, 0.1808, 0.1024, 0.192, 0.12, 0.2112, 0.0912, 0.1776,
0.0832, 0.1488
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Detection Prevalence`,
c(
0.3376, 0.3472, 0.3312, 0.3376, 0.328, 0.3312, 0.328, 0.3376,
0.3312, 0.3312
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Prevalence,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Prevalence`,
c(
0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376,
0.3376, 0.3376
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Neg Rate`,
c(
0.71957671957672, 0.497354497354497, 0.661375661375661, 0.44973544973545,
0.634920634920635, 0.343915343915344, 0.708994708994709, 0.486772486772487,
0.767195767195767, 0.560846560846561
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Neg Rate`,
c(0.72, 0.48, 0.68, 0.44, 0.64, 0.36, 0.72, 0.48, 0.76, 0.56),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Pos Rate`,
c(
0.363425925925926, 0.24537037037037, 0.342592592592593, 0.219907407407407,
0.31712962962963, 0.180555555555556, 0.358796296296296, 0.240740740740741,
0.377314814814815, 0.277777777777778
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Pos Rate`,
c(
0.370277777777778, 0.256111111111111, 0.347777777777778, 0.219722222222222,
0.311388888888889, 0.181666666666667, 0.356388888888889, 0.242222222222222,
0.371944444444444, 0.273333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Discovery Rate`,
c(
0.713095238095238, 0.487878787878788, 0.682539682539683, 0.437229437229437,
0.659259259259259, 0.355555555555556, 0.727380952380952, 0.468253968253968,
0.75462962962963, 0.55026455026455
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Discovery Rate`,
c(
0.713, 0.486909090909091, 0.692698412698413, 0.426493506493507,
0.656, 0.357333333333333, 0.729571428571429, 0.46, 0.743888888888889,
0.540952380952381
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Omission Rate`,
c(
0.365795206971678, 0.238095238095238, 0.337962962962963, 0.21957671957672,
0.312573099415205, 0.175219298245614, 0.354684095860566, 0.238716449242765,
0.379901960784314, 0.280092592592593
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Omission Rate`,
c(
0.37283660130719, 0.238095238095238, 0.35, 0.219365079365079,
0.316245614035088, 0.184236842105263, 0.361725490196078, 0.240035987404408,
0.380294117647059, 0.2825
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Threat Score`,
c(0.166199813258637, 0.342450142450142, 0.200396825396825, 0.385281385281385,
0.223443223443223, 0.475058275058275, 0.165079365079365, 0.345454545454545,
0.138095238095238, 0.283272283272283),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Weighted Threat Score`,
c(0.166162464985994, 0.352068376068376, 0.189761904761905, 0.394285714285714,
0.224175824175824, 0.465062937062937, 0.161142857142857, 0.351272727272727,
0.143809523809524, 0.287472527472527),
tolerance = 1e-4
)
expect_true(
as.character(multinom_baseline_random_eval_summ$Process[[1]]) %in%
paste0("---\nProcess Information\n---\nTarget column: targets_3\nPr",
"ediction columns: 1, 2, 3\nFamily / type: Multinomial\nClass",
"es: 1, 2, 3\nSoftmax: Not applied\nTarget counts: total=25, ",
"1=9, 2=7, 3=9\nPrediction counts: total=25, 1=10, 2=7, 3=8\n",
"Locale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
expect_equal(
multinom_baseline_random_eval_summ$Dependent,
rep("targets_3", 10)
)
expect_equal(
length(multinom_baseline_random_eval_summ$Predictions),
10
)
expect_equal(
length(multinom_baseline_random_eval_summ$`Confusion Matrix`),
10
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$Target,
as.character(c(1, 2, 2, 3, 1, 3, 3, 2, 2, 1, 1, 1, 2, 1, 2, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3))
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$`Predicted Class`,
c(
"2", "2", "3", "1", "2", "3", "1", "3", "3", "2", "3", "3",
"1", "2", "1", "1", "1", "1", "2", "1", "1", "3", "1", "2", "3"
)
)
preds <- dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions[[1]]$Prediction)
expect_equal(preds$`1`,
c(
0.0292644714588426, 0.0141597631269381, 0.298438639380654,
0.80940820303688, 0.0937005066362035, 0.35235101836276, 0.975413098798697,
0.175578647077284, 0.262771970923691, 0.00356192768281427, 0.0101813747120091,
0.124070740002606, 0.914437937270295, 0.0345747630499071, 0.443603716122321,
0.754396177250108, 0.622702608502644, 0.855071475511674, 0.149160434731838,
0.732327772925981, 0.704499634048166, 0.0395430319094823, 0.771078117207197,
0.311489940634356, 0.240482208017168
),
tolerance = 1e-4
)
expect_equal(preds$`2`,
c(
0.79920110839287, 0.864873705929184, 0.149529614257696, 0.0748305440084275,
0.472042382499711, 0.00209808185776226, 0.00727516403195308,
0.0559580981939634, 0.0479065485802701, 0.953560037716267, 0.0652172908723352,
0.252080524884308, 0.0636526992714326, 0.593734385511715, 0.263207821862484,
0.116691007284114, 0.0136173006382093, 0.119886570376107, 0.735943777020592,
0.0149628131301268, 0.00263470364120449, 0.0254377163472207,
0.125594895133848, 0.373604359020064, 0.284343108476632
),
tolerance = 1e-4
)
expect_equal(preds$`3`,
c(
0.171534420148287, 0.120966530943878, 0.55203174636165, 0.115761252954693,
0.434257110864085, 0.645550899779478, 0.0173117371693497, 0.768463254728752,
0.689321480496038, 0.0428780346009194, 0.924601334415656, 0.623848735113086,
0.0219093634582727, 0.371690851438378, 0.293188462015195, 0.128912815465778,
0.363680090859147, 0.0250419541122186, 0.11489578824757, 0.252709413943892,
0.29286566231063, 0.935019251743297, 0.103326987658955, 0.31490570034558,
0.4751746835062
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Prediction,
c("1", "2", "3", "1", "2", "3", "1", "2", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Target,
c("1", "1", "1", "2", "2", "2", "3", "3", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$N,
c(2L, 5L, 2L, 2L, 2L, 3L, 6L, 0L, 3L)
)
ROC <- multinom_baseline_random_eval_summ$ROC[[1]]
expect_equal(
names(ROC$rocs),
c("1/2", "1/3", "2/3")
)
expect_equal(
ROC$rocs$`1/2`[[1]]$sensitivities,
c(
1, 0.857142857142857, 0.857142857142857, 0.857142857142857,
0.714285714285714, 0.714285714285714, 0.571428571428571, 0.428571428571429,
0.285714285714286, 0.142857142857143, 0.142857142857143, 0.142857142857143,
0.142857142857143, 0.142857142857143, 0, 0, 0
)
)
expect_equal(
ROC$rocs$`1/2`[[2]]$sensitivities,
c(
1, 0.888888888888889, 0.888888888888889, 0.777777777777778,
0.777777777777778, 0.666666666666667, 0.555555555555556, 0.444444444444444,
0.444444444444444, 0.333333333333333, 0.333333333333333, 0.222222222222222,
0.222222222222222, 0.222222222222222, 0.222222222222222, 0.111111111111111,
0
)
)
expect_equal(
ROC$rocs$`1/2`[[1]]$specificities,
c(
0, 0, 0.111111111111111, 0.222222222222222, 0.222222222222222,
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667,
0.777777777777778, 0.777777777777778, 0.888888888888889, 1
)
)
expect_equal(
ROC$rocs$`1/2`[[2]]$specificities,
c(
0, 0, 0.142857142857143, 0.142857142857143, 0.285714285714286,
0.285714285714286, 0.285714285714286, 0.285714285714286, 0.428571428571429,
0.428571428571429, 0.571428571428571, 0.571428571428571, 0.714285714285714,
0.857142857142857, 1, 1, 1
)
)
}
# Random evaluations class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_class),
c(
"Repetition", "Class", "Balanced Accuracy", "Accuracy", "F1",
"Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score", "Support", "Confusion Matrix", "Family", "Dependent"
)
)
expect_equal(
length(multinom_baseline_random_eval_class$`Confusion Matrix`),
30
)
expect_equal(
colnames(multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]),
c("Class", "Prediction", "Target", "Pos_0", "Pos_1", "N")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Prediction,
c("0", "1", "0", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Target,
c("0", "0", "1", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_0,
c("TP", "FN", "FP", "TN")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_1,
c("TN", "FP", "FN", "TP")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$N,
c(8, 8, 7, 2)
)
expect_equal(
multinom_baseline_random_eval_class$Repetition,
rep(1:10, each = 3)
)
expect_equal(
multinom_baseline_random_eval_class$Class,
as.character(rep(1:3, 10))
)
expect_equal(sum(multinom_baseline_random_eval_class$`Balanced Accuracy`),
16.86855,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Accuracy),
18.32,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$F1),
12.3133,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Sensitivity),
12.50794,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Specificity),
21.22917,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Pos Pred Value`),
12.49174,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Neg Pred Value`),
21.29215,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Kappa),
3.675317,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Rate`),
4.16,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Prevalence`),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Prevalence),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Neg Rate`),
17.49206,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Pos Rate`),
8.770833,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Discovery Rate`),
17.50826,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Omission Rate`),
8.707853,
tolerance = 1e-4
)
expect_equal(
sum(multinom_baseline_random_eval_class$`Threat Score`),
8.17419329036976,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Support),
250,
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Family,
rep("binomial", 30),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Dependent,
rep("targets_3", 30),
tolerance = 1e-4
)
}
})
test_that("baseline() throws expected errors", {
# Binomial
xpectr::set_test_seed(1)
# cutoff
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = 1.1
),
"1 assertions failed:\n * Variable 'cutoff': Element 1 is not <= 1.",
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = NA
),
"1 assertions failed:\n * Variable 'cutoff': May not be NA.",
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = NULL
),
paste0(
"1 assertions failed:\n * Variable 'cutoff': Must be of type",
" 'number', not 'NULL'."
),
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = c(0, 1)
),
"1 assertions failed:\n * Variable 'cutoff': Must have length 1.",
fixed = T
)
# positive
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = NULL
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::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 = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = NA
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is 'NA'\n * checkmate::check_string(positive): May ",
"not be NA")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = 3
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is '3'\n * checkmate::check_string(positive): Must ",
"be of type 'string', not 'double'")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = -1
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is '-1'\n * checkmate::check_string(positive): Must",
" be of type 'string', not 'double'")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = c("0", "1")
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is not atomic scalar\n * checkmate::check_string(po",
"sitive): Must have length 1")),
fixed = T
)
expect_error(
xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = c(0, 1)
)),
xpectr::strip(paste0(
"Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is not atomic scalar\n * checkmate::check_string(po",
"sitive): Must be of type 'string', not 'double'")),
fixed = T
)
expect_message(baseline(
train_data = participant.scores,
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"'train_data' was not used for binomial version of baseline().",
fixed = T
)
expect_message(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
family = "binomial",
random_generator_fn = rnorm
),
paste0(
"'random_generator_fn' was not default function. ",
"Note that the 'random_generator_fn' is not used in ",
"the binomial version of baseline()."
),
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
metrics = list(
"AIC" = TRUE,
"AICc" = TRUE,
"BIC" = TRUE
),
family = "binomial"
),
"binomial baseline() does not accept the following metrics: AIC, AICc, BIC.",
fixed = T
)
expect_message(
suppressWarnings(baseline(
test_data = participant.scores,
train_data = participant.scores,
dependent_col = "score",
random_generator_fn = rnorm,
n = 10,
family = "gaussian",
parallel = FALSE
)),
paste0(
"'random_generator_fn' was not default function. ",
"Note that the 'random_generator_fn' is not used in ",
"the Gaussian version of baseline()."
)
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "score",
n = 10,
family = "gaussian"
),
paste0(
"1 assertions failed:\n * Variable 'train_data': Must be of ",
"type 'data.frame', not 'NULL'."
),
fixed = T
)
expect_error(
xpectr::strip_msg(
baseline(
train_data = participant.scores,
test_data = participant.scores,
dependent_col = "xxx",
n = 10,
family = "gaussian"
),
lowercase = TRUE
),
xpectr::strip(
paste0(
"2 assertions failed:\n * Variable 'colnames(test_data)': ",
ifelse(is_checkmate_v2_1(), "Names", ""),
" Must include the elements {xxx} ",
ifelse(is_checkmate_v2_1(), "but is missing elements {xxx}", ""),
"\n * Variable 'colnames(train_",
"data)': ",
ifelse(is_checkmate_v2_1(), "Names", ""),
" Must include the elements {xxx} ",
ifelse(is_checkmate_v2_1(), "but is missing elements {xxx}", "")
),
lowercase = TRUE
),
fixed = T
)
expect_error(
xpectr::strip_msg(
baseline(
train_data = participant.scores,
test_data = dplyr::select(participant.scores,-"score"),
dependent_col = "score",
n = 10,
family = "gaussian"
),
lowercase = TRUE
),
xpectr::strip(paste0("must include the elements {score}."), lowercase = TRUE),
fixed = T
)
expect_equal(
subtract_inf_count_from_na_count(tibble::tibble(
"Measure" = c("Mean", "NAs", "INFs", "Lol"),
"Accuracy" = c(1, 3, 4, 2),
"Balanced Accuracy" = c(3, 2, 5, 2)
)),
tibble::tibble(
"Measure" = c("Mean", "NAs", "INFs", "Lol"),
"Accuracy" = c(1, -1, 4, 2),
"Balanced Accuracy" = c(3, -3, 5, 2)
)
)
expect_error(xpectr::strip_msg(create_multinomial_baseline_evaluations(data.frame(),
dependent_col = "",
na.rm = NULL,
random_generator_fn = runif,
parallel_ = FALSE
)),
xpectr::strip(paste0("1 assertions failed:\n * Variable 'na.rm': Must be of type ",
"'logical flag', not 'NULL'.")),
fixed = TRUE
)
expect_error(xpectr::strip_msg(create_gaussian_baseline_evaluations(as.data.frame(matrix(1, 11, 1)),
data.frame(),
dependent_col = "",
na.rm = NULL
)),
xpectr::strip(paste0("1 assertions failed:\n * Variable 'na.rm': Must be of type ",
"'logical flag', not 'NULL'.")),
fixed = TRUE
)
xpectr::set_test_seed(1)
dat <- participant.scores
dat$diagnosis <- c(head(dat$diagnosis, 20), rep(2, 10))
expect_error(baseline(
test_data = dat,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"The dependent column must maximally contain 2 levels. Did you specify the correct family?",
fixed = T
)
dat$diagnosis <- as.character(dat$diagnosis)
expect_error(baseline(
test_data = dat,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"The dependent column must maximally contain 2 levels.",
fixed = T
)
})
test_that("multinomial baseline() works when test_data contains columns with names of the classes", {
# We simply test that we get different random evaluations
# when test_data contains columns with same names as the classes
# that could be interpreted as the probability columns
df <- tibble::tibble(
"Target" = rep(c("A", "B", "C"), 10),
"A" = rep(1, 30),
"B" = rep(0, 30),
"C" = rep(0, 30)
)
xpectr::set_test_seed(1)
multiclass_baseline <- df %>%
baseline(
dependent_col = "Target",
n = 3,
family = "multinomial",
parallel = FALSE
)
expect_equal(multiclass_baseline$random_evaluations$F1,
c(0.317714964773788, 0.338624338624339, 0.397326649958229),
tolerance = 1e-5
)
})
test_that("multinomial baseline() summarizes correctly with imbalanced dataset", {
testthat::skip_on_cran()
# We simply test that we get different random evaluations
# when test_data contains columns with same names as the classes
# that could be interpreted as the probability columns
df <- tibble::tibble("Target" = c(
rep("A", 1),
rep("A2", 1),
# rep("A3", 1),
rep("B", 50),
rep("C", 100)
))
xpectr::set_test_seed(3)
multiclass_baseline <- df %>%
baseline(
dependent_col = "Target",
n = 5,
family = "multinomial",
parallel = FALSE
)
means <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Mean",
] %>% base_deselect("Measure")
medians <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Median",
] %>% base_deselect("Measure")
mins <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Min",
] %>% base_deselect("Measure")
maxes <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Max",
] %>% base_deselect("Measure")
sds <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "SD",
] %>% base_deselect("Measure")
IQRs <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "IQR",
] %>% base_deselect("Measure")
apply_descriptor <- function(random_evals, fn) {
random_evals %>%
dplyr::summarise_if(is.numeric, .f = list(
~ fn(., na.rm = TRUE)
)) %>%
dplyr::select(-"Repetition")
}
rand_eval_means <- apply_descriptor(multiclass_baseline$random_evaluations, mean)
expect_equal(means, rand_eval_means, tolerance = 1e-5)
rand_eval_medians <- apply_descriptor(multiclass_baseline$random_evaluations, median)
expect_equal(medians, rand_eval_medians, tolerance = 1e-5)
rand_eval_sds <- apply_descriptor(multiclass_baseline$random_evaluations, sd)
expect_equal(sds, rand_eval_sds, tolerance = 1e-5)
rand_eval_IQRs <- apply_descriptor(multiclass_baseline$random_evaluations, IQR)
expect_equal(IQRs, rand_eval_IQRs, tolerance = 1e-5)
rand_eval_mins <- apply_descriptor(multiclass_baseline$random_evaluations, min)
expect_equal(mins, rand_eval_mins, tolerance = 1e-5)
rand_eval_maxes <- apply_descriptor(multiclass_baseline$random_evaluations, max)
expect_equal(maxes, rand_eval_maxes, tolerance = 1e-5)
expect_equal(multiclass_baseline$summarized_metrics$F1,
c(
0.225746415940381, 0.225746415940381, NA, 0, 0.225746415940381,
0.225746415940381, 4, 0, 0.428571428571428, 0.0512820512820513,
7, 0, NaN, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multiclass_baseline$random_evaluations$F1,
c(NaN, NaN, 0.225746415940381, NaN, NaN),
tolerance = 1e-5
)
expect_equal(legacy_unnest(multiclass_baseline$summarized_class_level_results)$F1,
c(
0.0512820512820513, 0.0512820512820513, NA, 0, 0.0512820512820513,
0.0512820512820513, 4, 0, NaN, 0.0130718954248366, 0.0622605363984674,
0.0622605363984674, 0.00948227484349775, 0.00670498084291188,
0.0689655172413793, 0.0555555555555556, 3, 0, NaN, 0.0130718954248366,
0.354358405547151, 0.354166666666667, 0.0402576466739213, 0.0614074406095921,
0.395348837209302, 0.301075268817204, 0, 0, NaN, 0.495049504950495,
0.378838880566167, 0.37410071942446, 0.0366196447359243, 0.0334224598930482,
0.428571428571428, 0.330827067669173, 0, 0, NaN, 0.793650793650794
),
tolerance = 1e-5
)
})
test_that("baseline wrappers minimal testing", {
testthat::skip_on_cran()
# These tests just show that calling with normal arg values doesn't fail
# That is, that the args are passed correctly to baseline()
# Gaussian ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(gauss_bsl <- baseline_gaussian(
test_data = participant.scores,
train_data = participant.scores,
dependent_col = "score",
n = 10,
metrics = "all",
random_effects = "(1|session)",
min_training_rows = 3,
min_training_rows_left_out = 3,
REML = FALSE,
parallel = FALSE
))
## Testing 'gauss_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(gauss_bsl$summarized_metrics),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
gauss_bsl$summarized_metrics[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_rows"),
fixed = TRUE)
expect_equal(
gauss_bsl$summarized_metrics[["RMSE"]],
c(14.98342, 13.41915, 4.93453, 0.79024, 28.97336, 12.83495, 0, 0,
12.84146),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MAE"]],
c(12.95187, 11.46669, 4.34472, 0.66819, 25.27778, 11.3, 0, 0, 11.3),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(RNG)"]],
c(0.21103, 0.189, 0.0695, 0.01113, 0.40808, 0.18077, 0, 0, 0.18087),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(IQR)"]],
c(0.53512, 0.47926, 0.17623, 0.02822, 1.03476, 0.45839, 0, 0, 0.45862),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(STD)"]],
c(0.77659, 0.69551, 0.25576, 0.04096, 1.50169, 0.66523, 0, 0, 0.66557),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(AVG)"]],
c(0.3865, 0.34615, 0.12729, 0.02038, 0.74738, 0.33108, 0, 0, 0.33125),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RSE"]],
c(0.68479, 0.50044, 0.57997, 0.05939, 2.33282, 0.4578, 0, 0, 0.45826),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RRSE"]],
c(0.78987, 0.7074, 0.26013, 0.04166, 1.52736, 0.67661, 0, 0, 0.67695),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RAE"]],
c(0.83286, 0.73736, 0.27938, 0.04297, 1.62546, 0.72664, 0, 0, 0.72664),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RMSLE"]],
c(0.41389, 0.3698, 0.12935, 0.04991, 0.77487, 0.34874, 0, 0, 0.35324),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MALE"]],
c(0.34758, 0.31415, 0.09475, 0.02261, 0.61514, 0.30837, 0, 0, 0.30837),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MAPE"]],
c(0.45503, 0.36922, 0.24845, 0.07511, 1.1531, 0.32215, 0, 0, 0.36212),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MSE"]],
c(246.41758, 180.08082, 208.70066, 21.3701, 839.45556, 164.73591,
0, 0, 164.903),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["TAE"]],
c(388.55611, 344.00073, 130.34157, 20.0457, 758.33333, 339, 0, 0,
339),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["TSE"]],
c(7392.52753, 5402.42463, 6261.01989, 641.10285, 25183.66667, 4942.07715,
0, 0, 4947.09013),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["r2m"]],
c(0, 0, 0, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["r2c"]],
c(0.42594, 0.46804, 0.17991, 0.07183, 0.68023, 0, 0, 0, 0.49599),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["AIC"]],
c(147.05357, 146.29569, 55.56405, 44.79092, 215.0563, 52.91061,
0, 0, 254.30188),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["AICc"]],
c(150.73341, 148.14184, 51.59965, 44.11329, 216.19916, 64.91061,
0, 0, 255.22496),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["BIC"]],
c(149.25056, 148.79533, 57.10576, 45.77481, 218.71293, 52.28589,
0, 0, 258.50547),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["Training Rows"]],
c(17, 17, 6.68331, 6.25, 25, 6, 0, 0, 30),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(gauss_bsl$summarized_metrics),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(gauss_bsl$summarized_metrics),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "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(gauss_bsl$summarized_metrics),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(gauss_bsl$summarized_metrics),
c(9L, 22L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(gauss_bsl$summarized_metrics)),
character(0),
fixed = TRUE)
## Finished testing 'gauss_bsl$summarized_metrics' ####
## Testing 'gauss_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(gauss_bsl$random_evaluations),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
gauss_bsl$random_evaluations[["RMSE"]],
c(13.64398, 13.07772, 12.83495, 13.22401, 13.99139, 28.97336, 13.3344,
13.02982, 14.22069, 13.50391),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MAE"]],
c(11.50297, 11.3, 11.3, 11.42285, 12.21149, 25.27778, 11.34728,
11.44461, 12.22296, 11.48878),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(RNG)"]],
c(0.19217, 0.18419, 0.18077, 0.18625, 0.19706, 0.40808, 0.18781,
0.18352, 0.20029, 0.1902),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(IQR)"]],
c(0.48728, 0.46706, 0.45839, 0.47229, 0.49969, 1.03476, 0.47623,
0.46535, 0.50788, 0.48228),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(STD)"]],
c(0.70717, 0.67782, 0.66523, 0.6854, 0.72517, 1.50169, 0.69112,
0.67533, 0.73706, 0.69991),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(AVG)"]],
c(0.35195, 0.33734, 0.33108, 0.34112, 0.36091, 0.74738, 0.34397,
0.33611, 0.36683, 0.34834),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RSE"]],
c(0.51733, 0.47528, 0.4578, 0.48597, 0.54401, 2.33282, 0.49412,
0.4718, 0.56199, 0.50676),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RRSE"]],
c(0.71926, 0.6894, 0.67661, 0.69712, 0.73757, 1.52736, 0.70294,
0.68688, 0.74966, 0.71187),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RAE"]],
c(0.73969, 0.72664, 0.72664, 0.73454, 0.78525, 1.62546, 0.72968,
0.73593, 0.78599, 0.73878),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RMSLE"]],
c(0.36902, 0.35433, 0.34874, 0.35807, 0.41539, 0.77487, 0.35172,
0.37058, 0.42158, 0.37456),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MALE"]],
c(0.31426, 0.30837, 0.30837, 0.31131, 0.33837, 0.61514, 0.3097,
0.31404, 0.34047, 0.31572),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MAPE"]],
c(0.35215, 0.35506, 0.35473, 0.3641, 0.4451, 1.1531, 0.32215, 0.38439,
0.44521, 0.37433),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MSE"]],
c(186.15812, 171.02681, 164.73591, 174.87451, 195.75907, 839.45556,
177.80617, 169.77619, 202.22804, 182.35547),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["TAE"]],
c(345.08913, 339, 339, 342.68544, 366.34467, 758.33333, 340.4183,
343.33815, 366.6888, 344.66331),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["TSE"]],
c(5584.74358, 5130.8042, 4942.07715, 5246.23543, 5872.77195, 25183.66667,
5334.18525, 5093.28575, 6066.84126, 5470.66402),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["r2m"]],
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["r2c"]],
c(0.52642, 0.40821, 0.49646, 0.45673, 0.4667, 0, 0.68023, 0.4793,
0.27596, 0.46938),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["AIC"]],
c(147.59485, 144.18876, 215.0563, 204.69939, 56.26379, 52.91061,
144.99653, 195.24105, 142.98879, 166.59566),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["AICc"]],
c(149.441, 146.18876, 216.19916, 205.89939, 68.26379, 64.91061,
146.84269, 196.50421, 144.98879, 168.09566),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["BIC"]],
c(150.09449, 146.50653, 218.71293, 208.23355, 55.63906, 52.28589,
147.49617, 198.64753, 145.30656, 169.58286),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["Training Rows"]],
c(17, 16, 25, 24, 6, 6, 17, 23, 16, 20),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["Dependent"]],
c("score", "score", "score", "score", "score", "score", "score",
"score", "score", "score"),
fixed = TRUE)
expect_equal(
gauss_bsl$random_evaluations[["Fixed"]],
c("1", "1", "1", "1", "1", "1", "1", "1", "1", "1"),
fixed = TRUE)
expect_equal(
gauss_bsl$random_evaluations[["Random"]],
c("(1|session)", "(1|session)", "(1|session)", "(1|session)", "(1|session)",
"(1|session)", "(1|session)", "(1|session)", "(1|session)",
"(1|session)"),
fixed = TRUE)
# Testing column names
expect_equal(
names(gauss_bsl$random_evaluations),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(gauss_bsl$random_evaluations),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"),
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list", "integer",
"character", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(gauss_bsl$random_evaluations),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "list", "list", "list", "integer", "character",
"character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(gauss_bsl$random_evaluations),
c(10L, 27L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(gauss_bsl$random_evaluations)),
character(0),
fixed = TRUE)
## Finished testing 'gauss_bsl$random_evaluations' ####
# Binomial ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(binom_bsl <- baseline_binomial(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
metrics = "all",
positive = "1",
cutoff = 0.6,
parallel = FALSE
))
## Testing 'binom_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(binom_bsl$summarized_metrics),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
binom_bsl$summarized_metrics[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0",
"All_1"),
fixed = TRUE)
expect_equal(
binom_bsl$summarized_metrics[["Balanced Accuracy"]],
c(0.48056, 0.47222, 0.08958, 0.10069, 0.61111, 0.31944, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Accuracy"]],
c(0.46, 0.46667, 0.09402, 0.125, 0.56667, 0.3, 0, 0, 0.4, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["F1"]],
c(0.44393, 0.52397, 0.14255, 0.22158, 0.60606, 0.23077, 0, 0, NaN,
0.75),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Sensitivity"]],
c(0.37778, 0.41667, 0.15449, 0.27778, 0.55556, 0.16667, 0, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Specificity"]],
c(0.58333, 0.58333, 0.14699, 0.22917, 0.83333, 0.41667, 0, 0, 1,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Pos Pred Value"]],
c(0.56712, 0.56696, 0.12928, 0.14062, 0.77778, 0.36364, 0, 0, NaN,
0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Neg Pred Value"]],
c(0.38664, 0.37747, 0.06811, 0.08333, 0.47619, 0.26316, 0, 0, 0.4,
NaN),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["AUC"]],
c(0.48426, 0.50231, 0.1345, 0.19329, 0.62963, 0.21759, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Lower CI"]],
c(0.27339, 0.28488, 0.12758, 0.19226, 0.42573, 0.04243, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Upper CI"]],
c(0.69513, 0.71975, 0.14244, 0.19431, 0.84678, 0.39276, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Kappa"]],
c(-0.0345, -0.05259, 0.16265, 0.18388, 0.19753, -0.32911, 0, 0,
0, 0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["MCC"]],
c(-0.04237, -0.057, 0.1866, 0.20975, 0.23757, -0.36711, 0, 0, 0,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Detection Rate"]],
c(0.22667, 0.25, 0.0927, 0.16667, 0.33333, 0.1, 0, 0, 0, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Detection Prevalence"]],
c(0.39333, 0.38333, 0.12353, 0.25, 0.53333, 0.23333, 0, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Prevalence"]],
c(0.6, 0.6, 0, 0, 0.6, 0.6, 0, 0, 0.6, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Neg Rate"]],
c(0.62222, 0.58333, 0.15449, 0.27778, 0.83333, 0.44444, 0, 0, 1,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Pos Rate"]],
c(0.41667, 0.41667, 0.14699, 0.22917, 0.58333, 0.16667, 0, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Discovery Rate"]],
c(0.43288, 0.43304, 0.12928, 0.14062, 0.63636, 0.22222, 0, 0, NaN,
0.4),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Omission Rate"]],
c(0.61336, 0.62253, 0.06811, 0.08333, 0.73684, 0.52381, 0, 0, 0.6,
NaN),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Threat Score"]],
c(0.29478, 0.355, 0.11526, 0.17874, 0.43478, 0.13043, 0, 0, 0, 0.6),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(binom_bsl$summarized_metrics),
c("Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI",
"Upper CI", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(binom_bsl$summarized_metrics),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(binom_bsl$summarized_metrics),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(binom_bsl$summarized_metrics),
c(10L, 21L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(binom_bsl$summarized_metrics)),
character(0),
fixed = TRUE)
## Finished testing 'binom_bsl$summarized_metrics' ####
## Testing 'binom_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(binom_bsl$random_evaluations),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
binom_bsl$random_evaluations[["Balanced Accuracy"]],
c(0.52778, 0.56944, 0.61111, 0.45833, 0.45833, 0.31944, 0.55556,
0.375, 0.44444, 0.48611),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Accuracy"]],
c(0.53333, 0.56667, 0.56667, 0.46667, 0.46667, 0.3, 0.53333, 0.33333,
0.4, 0.43333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["F1"]],
c(0.58824, 0.60606, 0.51852, 0.52941, 0.52941, 0.27586, 0.53333,
0.23077, 0.30769, 0.32),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Sensitivity"]],
c(0.55556, 0.55556, 0.38889, 0.5, 0.5, 0.22222, 0.44444, 0.16667,
0.22222, 0.22222),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Specificity"]],
c(0.5, 0.58333, 0.83333, 0.41667, 0.41667, 0.41667, 0.66667, 0.58333,
0.66667, 0.75),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Pos Pred Value"]],
c(0.625, 0.66667, 0.77778, 0.5625, 0.5625, 0.36364, 0.66667, 0.375,
0.5, 0.57143),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Neg Pred Value"]],
c(0.42857, 0.46667, 0.47619, 0.35714, 0.35714, 0.26316, 0.44444,
0.31818, 0.36364, 0.3913),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["AUC"]],
c(0.60185, 0.62963, 0.62963, 0.45833, 0.48611, 0.21759, 0.51852,
0.35648, 0.37963, 0.56481),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Lower CI"]],
c(0.39309, 0.42573, 0.41248, 0.24254, 0.27075, 0.04243, 0.29902,
0.13493, 0.17016, 0.34279),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Upper CI"]],
c(0.81062, 0.83353, 0.84678, 0.67413, 0.70148, 0.39276, 0.73802,
0.57803, 0.5891, 0.78684),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Kappa"]],
c(0.05405, 0.13333, 0.19753, -0.08108, -0.08108, -0.32911, 0.10256,
-0.21951, -0.09756, -0.0241),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["MCC"]],
c(0.05455, 0.13608, 0.23757, -0.08183, -0.08183, -0.36711, 0.11111,
-0.27696, -0.12309, -0.03217),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Detection Rate"]],
c(0.33333, 0.33333, 0.23333, 0.3, 0.3, 0.13333, 0.26667, 0.1, 0.13333,
0.13333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Detection Prevalence"]],
c(0.53333, 0.5, 0.3, 0.53333, 0.53333, 0.36667, 0.4, 0.26667, 0.26667,
0.23333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Prevalence"]],
c(0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Neg Rate"]],
c(0.44444, 0.44444, 0.61111, 0.5, 0.5, 0.77778, 0.55556, 0.83333,
0.77778, 0.77778),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Pos Rate"]],
c(0.5, 0.41667, 0.16667, 0.58333, 0.58333, 0.58333, 0.33333, 0.41667,
0.33333, 0.25),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Discovery Rate"]],
c(0.375, 0.33333, 0.22222, 0.4375, 0.4375, 0.63636, 0.33333, 0.625,
0.5, 0.42857),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Omission Rate"]],
c(0.57143, 0.53333, 0.52381, 0.64286, 0.64286, 0.73684, 0.55556,
0.68182, 0.63636, 0.6087),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Threat Score"]],
c(0.41667, 0.43478, 0.35, 0.36, 0.36, 0.16, 0.36364, 0.13043, 0.18182,
0.19048),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Dependent"]],
c("diagnosis", "diagnosis", "diagnosis", "diagnosis", "diagnosis",
"diagnosis", "diagnosis", "diagnosis", "diagnosis", "diagnosis"),
fixed = TRUE)
# Testing column names
expect_equal(
names(binom_bsl$random_evaluations),
c("Balanced Accuracy", "Accuracy", "F1", "Sensitivity", "Specificity",
"Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI", "Upper CI",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score", "Predictions", "ROC",
"Confusion Matrix", "Process", "Dependent"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(binom_bsl$random_evaluations),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list",
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(binom_bsl$random_evaluations),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "list", "list", "list", "list", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(binom_bsl$random_evaluations),
c(10L, 25L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(binom_bsl$random_evaluations)),
character(0),
fixed = TRUE)
## Finished testing 'binom_bsl$random_evaluations' ####
# Multinomial ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(multinom_bsl <- baseline_multinomial(
test_data = predicted.musicians %>% dplyr::filter(Classifier == "e1071_svm", `Fold Column` == ".folds_1"),
dependent_col = "Target",
n = 10,
metrics = "all",
random_generator_fn = rnorm,
parallel = FALSE
))
## Testing 'multinom_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19078 <- multinom_bsl$summarized_metrics
# Testing class
expect_equal(
class(output_19078),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19078[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "CL_Max",
"CL_Min", "CL_NAs", "CL_INFs", "All_A", "All_B", "All_C", "All_D"),
fixed = TRUE)
expect_equal(
output_19078[["Overall Accuracy"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Balanced Accuracy"]],
c(0.51667, 0.51111, 0.04099, 0.04722, 0.58889, 0.45556, 0, 0, 0.64444,
0.38889, 0, 0, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Balanced Accuracy"]],
c(0.51667, 0.51111, 0.04099, 0.04722, 0.58889, 0.45556, 0, 0, NA,
NA, NA, NA, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Accuracy"]],
c(0.6375, 0.63333, 0.03074, 0.03542, 0.69167, 0.59167, 0, 0, 0.76667,
0.55, 0, 0, 0.625, 0.625, 0.625, 0.625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Accuracy"]],
c(0.6375, 0.63333, 0.03074, 0.03542, 0.69167, 0.59167, 0, 0, NA,
NA, NA, NA, 0.625, 0.625, 0.625, 0.625),
tolerance = 1e-4)
expect_equal(
output_19078[["F1"]],
c(0.2719, 0.26527, 0.06209, 0.0739, 0.37897, 0.17845, 0, 0, 0.46154,
0.06897, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted F1"]],
c(0.2719, 0.26527, 0.06209, 0.0739, 0.37897, 0.17845, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Sensitivity"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, 0.53333,
0.06667, 0, 0, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Sensitivity"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Specificity"]],
c(0.75833, 0.75556, 0.0205, 0.02361, 0.79444, 0.72778, 0, 0, 0.88889,
0.66667, 0, 0, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Specificity"]],
c(0.75833, 0.75556, 0.0205, 0.02361, 0.79444, 0.72778, 0, 0, NA,
NA, NA, NA, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Pos Pred Value"]],
c(0.27442, 0.26546, 0.06486, 0.08217, 0.3837, 0.17897, 0, 0, 0.54545,
0.07143, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Pos Pred Value"]],
c(0.27442, 0.26546, 0.06486, 0.08217, 0.3837, 0.17897, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Neg Pred Value"]],
c(0.75864, 0.75571, 0.02063, 0.02323, 0.79555, 0.72796, 0, 0, 0.82051,
0.69565, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Neg Pred Value"]],
c(0.75864, 0.75571, 0.02063, 0.02323, 0.79555, 0.72796, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["AUC"]],
c(0.50715, 0.50241, 0.03493, 0.04861, 0.55222, 0.44815, 0, 0, NA,
NA, NA, NA, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Kappa"]],
c(0.03264, 0.02171, 0.08248, 0.09823, 0.17601, -0.09015, 0, 0, 0.31707,
-0.22727, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Kappa"]],
c(0.03264, 0.02171, 0.08248, 0.09823, 0.17601, -0.09015, 0, 0, NA,
NA, NA, NA, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["MCC"]],
c(0.03362, 0.02226, 0.08268, 0.09547, 0.17938, -0.08962, 0, 0, NA,
NA, NA, NA, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["Detection Rate"]],
c(0.06875, 0.06667, 0.01537, 0.01771, 0.09583, 0.04583, 0, 0, 0.13333,
0.01667, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Detection Rate"]],
c(0.06875, 0.06667, 0.01537, 0.01771, 0.09583, 0.04583, 0, 0, NA,
NA, NA, NA, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Detection Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.35, 0.16667, 0, 0, 0.25,
0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Detection Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, NA, NA, NA, NA, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, NA, NA, NA, NA, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["False Neg Rate"]],
c(0.725, 0.73333, 0.06149, 0.07083, 0.81667, 0.61667, 0, 0, 0.93333,
0.46667, 0, 0, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Neg Rate"]],
c(0.725, 0.73333, 0.06149, 0.07083, 0.81667, 0.61667, 0, 0, NA,
NA, NA, NA, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["False Pos Rate"]],
c(0.24167, 0.24444, 0.0205, 0.02361, 0.27222, 0.20556, 0, 0, 0.33333,
0.11111, 0, 0, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Pos Rate"]],
c(0.24167, 0.24444, 0.0205, 0.02361, 0.27222, 0.20556, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["False Discovery Rate"]],
c(0.72558, 0.73454, 0.06486, 0.08217, 0.82103, 0.6163, 0, 0, 0.92857,
0.45455, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Discovery Rate"]],
c(0.72558, 0.73454, 0.06486, 0.08217, 0.82103, 0.6163, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["False Omission Rate"]],
c(0.24136, 0.24429, 0.02063, 0.02323, 0.27204, 0.20445, 0, 0, 0.30435,
0.17949, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Omission Rate"]],
c(0.24136, 0.24429, 0.02063, 0.02323, 0.27204, 0.20445, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Threat Score"]],
c(0.16053, 0.15405, 0.04233, 0.04668, 0.23562, 0.09963, 0, 0, 0.3,
0.03571, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Threat Score"]],
c(0.16053, 0.15405, 0.04233, 0.04668, 0.23562, 0.09963, 0, 0, NA,
NA, NA, NA, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19078),
c("Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate", "Detection Prevalence",
"Weighted Detection Prevalence", "Prevalence", "Weighted Prevalence",
"False Neg Rate", "Weighted False Neg Rate", "False Pos Rate",
"Weighted False Pos Rate", "False Discovery Rate", "Weighted False Discovery Rate",
"False Omission Rate", "Weighted False Omission Rate", "Threat Score",
"Weighted Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::smpl(xpectr::element_classes(output_19078), n = 30),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::smpl(xpectr::element_types(output_19078), n = 30),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19078),
c(16L, 36L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19078)),
character(0),
fixed = TRUE)
## Finished testing 'multinom_bsl$summarized_metrics' ####
summ_class_level <- legacy_unnest(multinom_bsl$summarized_class_level_results)
## Testing 'summ_class_level' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(summ_class_level),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(summ_class_level[["Class"]], n = 30),
c("A", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B",
"B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D",
"D", "D", "D", "D", "D"),
fixed = TRUE)
expect_equal(
xpectr::smpl(summ_class_level[["Measure"]], n = 30),
c("Median", "SD", "Max", "Min", "NAs", "INFs", "All_1", "Mean",
"Median", "SD", "IQR", "NAs", "INFs", "All_0", "Mean", "Median",
"SD", "IQR", "Max", "Min", "NAs", "All_0", "All_1", "Mean",
"Median", "Max", "Min", "NAs", "INFs", "All_1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(summ_class_level[["Balanced Accuracy"]], n = 30),
c(0.49444, 0.05787, 0.56667, 0.38889, 0, 0, 0.5, 0.53444, 0.54444,
0.05429, 0.09167, 0, 0, 0.5, 0.50889, 0.48889, 0.03621, 0.04444,
0.57778, 0.47778, 0, 0.5, 0.5, 0.53111, 0.52778, 0.64444, 0.41111,
0, 0, 0.5),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Accuracy"]], n = 30),
c(0.63333, 0.03518, 0.66667, 0.55, 0, 0, 0.25, 0.645, 0.65, 0.03932,
0.04167, 0, 0, 0.75, 0.63333, 0.63333, 0.03685, 0.04167, 0.7,
0.58333, 0, 0.75, 0.25, 0.64333, 0.63333, 0.76667, 0.55, 0,
0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["F1"]], n = 30),
c(0.21825, 0.10232, 0.36364, 0.06897, 0, 0, 0.4, 0.3005, 0.33272,
0.09092, 0.12679, 0, 0, NaN, 0.26065, 0.24621, 0.0561, 0.06845,
0.35714, 0.2069, 0, NaN, 0.4, 0.30387, 0.30777, 0.46154, 0.12903,
0, 0, 0.4),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Sensitivity"]], n = 30),
c(0.2, 0.1178, 0.4, 0.06667, 0, 0, 1, 0.31333, 0.33333, 0.1178,
0.16667, 0, 0, 0, 0.26, 0.26667, 0.0663, 0.06667, 0.4, 0.2,
0, 0, 1, 0.30667, 0.33333, 0.4, 0.13333, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Specificity"]], n = 30),
c(0.77778, 0.03506, 0.8, 0.71111, 0, 0, 0, 0.75556, 0.75556, 0.05342,
0.08889, 0, 0, 1, 0.75778, 0.76667, 0.04964, 0.08333, 0.82222,
0.68889, 0, 1, 0, 0.75556, 0.72222, 0.88889, 0.66667, 0, 0,
0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Pos Pred Value"]], n = 30),
c(0.24038, 0.09222, 0.33333, 0.07143, 0, 0, 0.25, 0.29479, 0.30625,
0.07518, 0.12693, 0, 0, NaN, 0.26524, 0.23529, 0.05801, 0.06857,
0.38462, 0.21429, 0, NaN, 0.25, 0.30858, 0.29706, 0.54545, 0.125,
0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Neg Pred Value"]], n = 30),
c(0.74734, 0.02882, 0.78571, 0.69565, 0, 0, NaN, 0.76841, 0.77386,
0.02903, 0.04358, 0, 0, 0.75, 0.75435, 0.74468, 0.01809, 0.02144,
0.78723, 0.7381, 0, 0.75, NaN, 0.76468, 0.76467, 0.81633, 0.70455,
0, 0, NaN),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Kappa"]], n = 30),
c(-0.01163, 0.11716, 0.125, -0.22727, 0, 0, 0, 0.06521, 0.08348,
0.10383, 0.17704, 0, 0, 0, 0.01839, -0.02128, 0.07333, 0.09468,
0.16279, -0.04545, 0, 0, 0, 0.06647, 0.05691, 0.31707, -0.17391,
0, 0, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Detection Rate"]], n = 30),
c(0.05, 0.02945, 0.1, 0.01667, 0, 0, 0.25, 0.07833, 0.08333, 0.02945,
0.04167, 0, 0, 0, 0.065, 0.06667, 0.01657, 0.01667, 0.1, 0.05,
0, 0, 0.25, 0.07667, 0.08333, 0.1, 0.03333, 0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Detection Prevalence"]], n = 30),
c(0.21667, 0.04335, 0.31667, 0.18333, 0, 0, 1, 0.26167, 0.26667,
0.05829, 0.075, 0, 0, 0, 0.24667, 0.225, 0.04431, 0.06667, 0.31667,
0.2, 0, 0, 1, 0.26, 0.275, 0.31667, 0.16667, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Prevalence"]], n = 30),
c(0.25, 0, 0.25, 0.25, 0, 0, 0.25, 0.25, 0.25, 0, 0, 0, 0, 0.25,
0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Neg Rate"]], n = 30),
c(0.8, 0.1178, 0.93333, 0.6, 0, 0, 0, 0.68667, 0.66667, 0.1178,
0.16667, 0, 0, 1, 0.74, 0.73333, 0.0663, 0.06667, 0.8, 0.6,
0, 1, 0, 0.69333, 0.66667, 0.86667, 0.6, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Pos Rate"]], n = 30),
c(0.22222, 0.03506, 0.28889, 0.2, 0, 0, 1, 0.24444, 0.24444, 0.05342,
0.08889, 0, 0, 0, 0.24222, 0.23333, 0.04964, 0.08333, 0.31111,
0.17778, 0, 0, 1, 0.24444, 0.27778, 0.33333, 0.11111, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Discovery Rate"]], n = 30),
c(0.75962, 0.09222, 0.92857, 0.66667, 0, 0, 0.75, 0.70521, 0.69375,
0.07518, 0.12693, 0, 0, NaN, 0.73476, 0.76471, 0.05801, 0.06857,
0.78571, 0.61538, 0, NaN, 0.75, 0.69142, 0.70294, 0.875, 0.45455,
0, 0, 0.75),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Omission Rate"]], n = 30),
c(0.25266, 0.02882, 0.30435, 0.21429, 0, 0, NaN, 0.23159, 0.22614,
0.02903, 0.04358, 0, 0, 0.25, 0.24565, 0.25532, 0.01809, 0.02144,
0.2619, 0.21277, 0, 0.25, NaN, 0.23532, 0.23533, 0.29545, 0.18367,
0, 0, NaN),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Threat Score"]], n = 30),
c(0.1225, 0.06455, 0.22222, 0.03571, 0, 0, 0.25, 0.17984, 0.1996,
0.06288, 0.08689, 0, 0, 0, 0.15096, 0.14039, 0.03816, 0.0449,
0.21739, 0.11538, 0, 0, 0.25, 0.18274, 0.18188, 0.3, 0.06897,
0, 0, 0.25),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(summ_class_level),
c("Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "Kappa",
"Detection Rate", "Detection Prevalence", "Prevalence", "False Neg Rate",
"False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(summ_class_level),
c("character", "character", "numeric", "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(summ_class_level),
c("character", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(summ_class_level),
c(40L, 18L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(summ_class_level)),
character(0),
fixed = TRUE)
## Finished testing 'summ_class_level' ####
## Testing 'multinom_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- multinom_bsl$random_evaluations
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Repetition"]],
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
tolerance = 1e-4)
expect_equal(
output_19148[["Overall Accuracy"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Balanced Accuracy"]],
c(0.47778, 0.45556, 0.54444, 0.48889, 0.56667, 0.5, 0.52222, 0.5,
0.52222, 0.58889),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Balanced Accuracy"]],
c(0.47778, 0.45556, 0.54444, 0.48889, 0.56667, 0.5, 0.52222, 0.5,
0.52222, 0.58889),
tolerance = 1e-4)
expect_equal(
output_19148[["Accuracy"]],
c(0.60833, 0.59167, 0.65833, 0.61667, 0.675, 0.625, 0.64167, 0.625,
0.64167, 0.69167),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Accuracy"]],
c(0.60833, 0.59167, 0.65833, 0.61667, 0.675, 0.625, 0.64167, 0.625,
0.64167, 0.69167),
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
c(0.21634, 0.17845, 0.3107, 0.22481, 0.35094, 0.24923, 0.28131,
0.24516, 0.2831, 0.37897),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted F1"]],
c(0.21634, 0.17845, 0.3107, 0.22481, 0.35094, 0.24923, 0.28131,
0.24516, 0.2831, 0.37897),
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Sensitivity"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
c(0.73889, 0.72778, 0.77222, 0.74444, 0.78333, 0.75, 0.76111, 0.75,
0.76111, 0.79444),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Specificity"]],
c(0.73889, 0.72778, 0.77222, 0.74444, 0.78333, 0.75, 0.76111, 0.75,
0.76111, 0.79444),
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
c(0.2197, 0.17897, 0.31667, 0.2211, 0.36124, 0.25015, 0.28078, 0.24494,
0.28692, 0.3837),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Pos Pred Value"]],
c(0.2197, 0.17897, 0.31667, 0.2211, 0.36124, 0.25015, 0.28078, 0.24494,
0.28692, 0.3837),
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
c(0.73842, 0.72796, 0.77289, 0.74575, 0.78305, 0.74992, 0.76143,
0.75058, 0.76083, 0.79555),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Neg Pred Value"]],
c(0.73842, 0.72796, 0.77289, 0.74575, 0.78305, 0.74992, 0.76143,
0.75058, 0.76083, 0.79555),
tolerance = 1e-4)
expect_equal(
output_19148[["AUC"]],
c(0.46704, 0.50222, 0.53333, 0.50259, 0.53778, 0.44815, 0.55222,
0.48556, 0.49556, 0.54704),
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
c(-0.04286, -0.09015, 0.08756, -0.02743, 0.1362, 0.00018, 0.04325,
-0.00217, 0.0458, 0.17601),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Kappa"]],
c(-0.04286, -0.09015, 0.08756, -0.02743, 0.1362, 0.00018, 0.04325,
-0.00217, 0.0458, 0.17601),
tolerance = 1e-4)
expect_equal(
output_19148[["MCC"]],
c(-0.04469, -0.08962, 0.09003, -0.02238, 0.13433, 0, 0.04453, 0,
0.04466, 0.17938),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
c(0.05417, 0.04583, 0.07917, 0.05833, 0.0875, 0.0625, 0.07083, 0.0625,
0.07083, 0.09583),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Detection Rate"]],
c(0.05417, 0.04583, 0.07917, 0.05833, 0.0875, 0.0625, 0.07083, 0.0625,
0.07083, 0.09583),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Detection Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["False Neg Rate"]],
c(0.78333, 0.81667, 0.68333, 0.76667, 0.65, 0.75, 0.71667, 0.75,
0.71667, 0.61667),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Neg Rate"]],
c(0.78333, 0.81667, 0.68333, 0.76667, 0.65, 0.75, 0.71667, 0.75,
0.71667, 0.61667),
tolerance = 1e-4)
expect_equal(
output_19148[["False Pos Rate"]],
c(0.26111, 0.27222, 0.22778, 0.25556, 0.21667, 0.25, 0.23889, 0.25,
0.23889, 0.20556),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Pos Rate"]],
c(0.26111, 0.27222, 0.22778, 0.25556, 0.21667, 0.25, 0.23889, 0.25,
0.23889, 0.20556),
tolerance = 1e-4)
expect_equal(
output_19148[["False Discovery Rate"]],
c(0.7803, 0.82103, 0.68333, 0.7789, 0.63876, 0.74985, 0.71922, 0.75506,
0.71308, 0.6163),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Discovery Rate"]],
c(0.7803, 0.82103, 0.68333, 0.7789, 0.63876, 0.74985, 0.71922, 0.75506,
0.71308, 0.6163),
tolerance = 1e-4)
expect_equal(
output_19148[["False Omission Rate"]],
c(0.26158, 0.27204, 0.22711, 0.25425, 0.21695, 0.25008, 0.23857,
0.24942, 0.23917, 0.20445),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Omission Rate"]],
c(0.26158, 0.27204, 0.22711, 0.25425, 0.21695, 0.25008, 0.23857,
0.24942, 0.23917, 0.20445),
tolerance = 1e-4)
expect_equal(
output_19148[["Threat Score"]],
c(0.12137, 0.09963, 0.18485, 0.13015, 0.21626, 0.14333, 0.16437,
0.14373, 0.16595, 0.23562),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Threat Score"]],
c(0.12137, 0.09963, 0.18485, 0.13015, 0.21626, 0.14333, 0.16437,
0.14373, 0.16595, 0.23562),
tolerance = 1e-4)
expect_equal(
output_19148[["Dependent"]],
c("Target", "Target", "Target", "Target", "Target", "Target", "Target",
"Target", "Target", "Target"),
fixed = TRUE)
# Testing column names
expect_equal(
names(output_19148),
c("Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate", "Detection Prevalence",
"Weighted Detection Prevalence", "Prevalence", "Weighted Prevalence",
"False Neg Rate", "Weighted False Neg Rate", "False Pos Rate",
"Weighted False Pos Rate", "False Discovery Rate", "Weighted False Discovery Rate",
"False Omission Rate", "Weighted False Omission Rate", "Threat Score",
"Weighted Threat Score", "Predictions", "ROC", "Confusion Matrix",
"Class Level Results", "Process", "Dependent"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::smpl(xpectr::element_classes(output_19148), n = 30),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list",
ifelse(is_dplyr_1(), "vctrs_list_of", "list"),
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::smpl(xpectr::element_types(output_19148), n = 30),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"list", "list", "list", "list", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(10L, 42L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'multinom_bsl$random_evaluations' ####
})
LudvigOlsen/cvms documentation built on March 2, 2024, 1:54 p.m.
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library(cvms)
context("baseline()")
test_that("binomial evaluations are correct in baseline()", {
# We're too near the test time limit on CRAN
# So skipping this and relying on local and github actions
# to test this
testthat::skip_on_cran()
xpectr::set_test_seed(1)
binom_baseline <- baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
metrics = "all",
parallel = FALSE
)
binom_baseline_summ <- binom_baseline$summarized_metrics
binom_baseline_reval <- binom_baseline$random_evaluations
expect_equal(
colnames(binom_baseline_summ),
c("Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI",
"Upper CI", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score")
)
expect_equal(
colnames(binom_baseline_reval),
c("Balanced Accuracy", "Accuracy", "F1", "Sensitivity", "Specificity",
"Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI", "Upper CI",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score", "Predictions", "ROC", "Confusion Matrix", "Process",
"Dependent")
)
# Summarized results
expect_equal(binom_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_0", "All_1"
))
expect_equal(binom_baseline_summ$`Balanced Accuracy`,
c(
0.493055555555556, 0.472222222222222, 0.0940282222758792, 0.111111111111111,
0.680555555555556, 0.347222222222222, 0, 0, 0.5, 0.5
),
tolerance = 1e-4
)
expect_equal(binom_baseline_summ$Accuracy,
c(
0.486666666666667, 0.466666666666667, 0.102077191803163, 0.116666666666667,
0.666666666666667, 0.3, 0, 0, 0.4, 0.6
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$F1,
c(
0.5080706, 0.5294118, 0.1486011, 0.1303922, 0.6875000,
0.1600000, 0.0000000, 0.0000000, NA, 0.7500000
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Pos Pred Value`,
c(
0.5751821, 0.5729167, 0.1299251, 0.1009191,
0.7857143, 0.2857143, 0.0, 0.0, NaN, 0.60
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Neg Pred Value`,
c(
0.39934657, 0.37301587, 0.07978087, 0.11401099,
0.5625, 0.30434783, 0.0, 0.0, 0.40, NaN
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$AUC,
c(
0.54907407, 0.54398148, 0.08210109, 0.09143519,
0.69907407, 0.43055556, 0.0, 0.0, 0.50, 0.50
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Lower CI`,
c(
0.324540013310847, 0.323088934387213, 0.0886239631372627, 0.110594163133731,
0.484635549804418, 0.20545074066643, 0, 0, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Upper CI`,
c(
0.773608134837302, 0.7593316803908, 0.0766521660833089, 0.0815468441468371,
0.91351259834373, 0.655660370444682, 0, 0, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$Kappa,
c(
-0.0100961224305358, -0.0533610533610534, 0.174558224749766,
0.215975564605702, 0.342105263157895, -0.265060240963855, 0,
0, 0, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$MCC,
c(
-0.0193021509301018, -0.0548047433081375, 0.194735989045363,
0.218543873422503, 0.354604071633488, -0.353919198648283, 0,
0, 0, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Detection Rate`,
c(
0.276666666666667, 0.3, 0.0916919157145678, 0.0916666666666667,
0.366666666666667, 0.0666666666666667, 0, 0, 0, 0.6
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Detection Prevalence`,
c(
0.466666666666667, 0.5, 0.0993807989999906, 0.116666666666667,
0.566666666666667, 0.233333333333333, 0, 0, 0, 1
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$Prevalence,
c(0.6, 0.6, 0, 0, 0.6, 0.6, 0, 0, 0.6, 0.6),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Neg Rate`,
c(
0.538888888888889, 0.5, 0.15281985952428, 0.152777777777778,
0.888888888888889, 0.388888888888889, 0, 0, 1, 0
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Pos Rate`,
c(
0.475, 0.5, 0.104305463086214, 0.145833333333333, 0.583333333333333,
0.25, 0, 0, 0, 1
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Discovery Rate`,
c(
0.424817927170868, 0.427083333333333, 0.129925068392218, 0.100919117647059,
0.714285714285714, 0.214285714285714, 0, 0, NaN, 0.4
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`False Omission Rate`,
c(
0.600653434729522, 0.626984126984127, 0.0797808713464425, 0.114010989010989,
0.695652173913043, 0.4375, 0, 0, 0.6, NaN
),
tolerance = 1e-5
)
expect_equal(binom_baseline_summ$`Threat Score`,
c(0.351489648033126, 0.36, 0.122896613634581, 0.121992753623188,
0.523809523809524, 0.0869565217391304, 0, 0, 0, 0.6),
tolerance = 1e-5
)
# The random evaluations
expect_equal(binom_baseline_reval$`Balanced Accuracy`,
c(
0.6805556, 0.5277778, 0.4861111, 0.4305556, 0.5555556,
0.4583333, 0.4583333, 0.5694444, 0.4166667, 0.3472222
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Accuracy,
c(
0.666666666666667, 0.533333333333333, 0.466666666666667, 0.433333333333333,
0.566666666666667, 0.466666666666667, 0.466666666666667, 0.566666666666667,
0.4, 0.3
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$F1,
c(
0.6875, 0.5882353, 0.4666667, 0.4848485, 0.6285714,
0.5294118, 0.5294118, 0.6060606, 0.40, 0.160
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Sensitivity,
c(
0.611111111111111, 0.555555555555556, 0.388888888888889, 0.444444444444444,
0.611111111111111, 0.5, 0.5, 0.555555555555556, 0.333333333333333,
0.111111111111111
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Specificity,
c(
0.75, 0.5, 0.583333333333333, 0.416666666666667, 0.5, 0.416666666666667,
0.416666666666667, 0.583333333333333, 0.5, 0.583333333333333
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Pos Pred Value`,
c(
0.785714285714286, 0.625, 0.583333333333333, 0.533333333333333,
0.647058823529412, 0.5625, 0.5625, 0.666666666666667, 0.5, 0.285714285714286
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Neg Pred Value`,
c(
0.5625, 0.428571428571429, 0.388888888888889, 0.333333333333333,
0.461538461538462, 0.357142857142857, 0.357142857142857, 0.466666666666667,
0.333333333333333, 0.304347826086957
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$AUC,
c(
0.6990741, 0.5925926, 0.6527778, 0.500, 0.5555556,
0.4305556, 0.5601852, 0.5324074, 0.4907407, 0.4768519
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Lower CI`,
c(
0.484635549804418, 0.368049842933427, 0.436226588192728, 0.275453328641993,
0.325144150211566, 0.20545074066643, 0.345488105840725, 0.32103371856286,
0.243935884488029, 0.239982223766291
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Upper CI`,
c(
0.91351259834373, 0.817135342251759, 0.869328967362827, 0.724546671358007,
0.785966960899545, 0.655660370444682, 0.774882264529645, 0.743781096251955,
0.737545596993452, 0.713721479937413
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Kappa,
c(
0.342105263157895, 0.054054054054054, -0.0256410256410256,
-0.133333333333333, 0.10958904109589, -0.0810810810810812, -0.0810810810810812,
0.133333333333333, -0.153846153846154, -0.265060240963855
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$MCC,
c(
0.354604071633488, 0.0545544725589981, -0.0277777777777778,
-0.136082763487954, 0.109847007276218, -0.0818317088384971, -0.0818317088384971,
0.136082763487954, -0.166666666666667, -0.353919198648283
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Detection Rate`,
c(
0.366666666666667, 0.333333333333333, 0.233333333333333, 0.266666666666667,
0.366666666666667, 0.3, 0.3, 0.333333333333333, 0.2, 0.0666666666666667
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Detection Prevalence`,
c(
0.466666666666667, 0.533333333333333, 0.4, 0.5, 0.566666666666667,
0.533333333333333, 0.533333333333333, 0.5, 0.4, 0.233333333333333
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$Prevalence,
c(0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Neg Rate`,
c(
0.388888888888889, 0.444444444444444, 0.611111111111111, 0.555555555555556,
0.388888888888889, 0.5, 0.5, 0.444444444444444, 0.666666666666667,
0.888888888888889
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Pos Rate`,
c(
0.25, 0.5, 0.416666666666667, 0.583333333333333, 0.5, 0.583333333333333,
0.583333333333333, 0.416666666666667, 0.5, 0.416666666666667
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Discovery Rate`,
c(
0.214285714285714, 0.375, 0.416666666666667, 0.466666666666667,
0.352941176470588, 0.4375, 0.4375, 0.333333333333333, 0.5, 0.714285714285714
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`False Omission Rate`,
c(
0.4375, 0.571428571428571, 0.611111111111111, 0.666666666666667,
0.538461538461538, 0.642857142857143, 0.642857142857143, 0.533333333333333,
0.666666666666667, 0.695652173913043
),
tolerance = 1e-3
)
expect_equal(binom_baseline_reval$`Threat Score`,
c(0.523809523809524, 0.416666666666667, 0.304347826086957, 0.32,
0.458333333333333, 0.36, 0.36, 0.434782608695652, 0.25, 0.0869565217391304
),
tolerance = 1e-3
)
expect_equal(
binom_baseline_reval$Dependent,
rep("diagnosis", 10)
)
expect_equal(length(binom_baseline_reval$Predictions), 10)
all_predictions <- dplyr::bind_rows(binom_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 30))
)
expect_equal(sum(all_predictions$Target), 180)
expect_equal(sum(as.numeric(all_predictions$`Predicted Class`)), 140)
expect_equal(sum(all_predictions$Prediction), 147.0334, tolerance = 1e-3)
## Testing 'all_predictions' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_predictions),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(all_predictions[["Repetition"]], n = 30),
c(1, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5, 6, 6, 7, 7, 7, 7, 8, 8, 9, 9,
9, 9, 9, 9, 10, 10, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Target"]], n = 30),
c(1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 1, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Prediction"]], n = 30),
c(0.65167, 0.18622, 0.72371, 0.41127, 0.33377, 0.20269, 0.12937,
0.49559, 0.03554, 0.6428, 0.60154, 0.45313, 0.07528, 0.54765,
0.38891, 0.51858, 0.12916, 0.64958, 0.25762, 0.42875, 0.13269,
0.60359, 0.27797, 0.75471, 0.16958, 0.29865, 0.18123, 0.02779,
0.22131, 0.92925),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_predictions[["Predicted Class"]], n = 30),
c("1", "0", "1", "0", "0", "0", "0", "0", "0", "1", "1", "0", "0",
"1", "0", "1", "0", "1", "0", "0", "0", "1", "0", "1", "0",
"0", "0", "0", "0", "1"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_predictions),
c("Repetition", "Target", "Prediction", "Predicted Class"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_predictions),
c("integer", "numeric", "numeric", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_predictions),
c("integer", "double", "double", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_predictions),
c(300L, 4L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_predictions)),
character(0),
fixed = TRUE)
## Finished testing 'all_predictions' ####
all_confmats <- dplyr::bind_rows(binom_baseline_reval$`Confusion Matrix`)
expect_equal(sum(as.numeric(all_confmats$Prediction)), 20, tolerance = 1e-3)
expect_equal(sum(as.numeric(all_confmats$Target)), 20, tolerance = 1e-3)
expect_equal(sum(all_confmats$N), 300, tolerance = 1e-3)
expect_equal(sum(all_confmats$Repetition), 220, tolerance = 1e-3)
## Testing 'all_confmats' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_confmats),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(all_confmats[["Repetition"]], n = 30),
c(1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7,
8, 8, 8, 8, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(all_confmats[["Prediction"]], n = 30),
c("1", "0", "0", "1", "0", "1", "1", "0", "1", "0", "1", "0", "1",
"0", "0", "1", "0", "1", "0", "1", "0", "0", "1", "0", "1",
"0", "1", "0", "1", "1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Target"]], n = 30),
c("0", "1", "0", "0", "1", "1", "0", "1", "1", "0", "0", "0", "0",
"1", "0", "0", "1", "1", "0", "0", "1", "0", "0", "1", "1",
"1", "1", "0", "0", "1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Pos_0"]], n = 30),
c("FN", "FP", "TP", "FN", "FP", "TN", "FN", "FP", "TN", "TP", "FN",
"TP", "FN", "FP", "TP", "FN", "FP", "TN", "TP", "FN", "FP",
"TP", "FN", "FP", "TN", "FP", "TN", "TP", "FN", "TN"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["Pos_1"]], n = 30),
c("FP", "FN", "TN", "FP", "FN", "TP", "FP", "FN", "TP", "TN", "FP",
"TN", "FP", "FN", "TN", "FP", "FN", "TP", "TN", "FP", "FN",
"TN", "FP", "FN", "TP", "FN", "TP", "TN", "FP", "TP"),
fixed = TRUE)
expect_equal(
xpectr::smpl(all_confmats[["N"]], n = 30),
c(3, 7, 6, 6, 8, 10, 5, 11, 7, 5, 7, 6, 6, 7, 5, 7, 9, 9, 5, 7,
9, 7, 5, 8, 10, 12, 6, 7, 5, 2),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(all_confmats),
c("Repetition", "Prediction", "Target", "Pos_0", "Pos_1", "N"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_confmats),
c("integer", "character", "character", "character", "character",
"integer"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_confmats),
c("integer", "character", "character", "character", "character",
"integer"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_confmats),
c(40L, 6L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_confmats)),
character(0),
fixed = TRUE)
## Finished testing 'all_confmats' ####
# Test first 'Process'
## Testing 'binom_baseline_reval$Process[[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_19148 <- as.list(binom_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"process_info_binomial",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "list")
# Testing names
expect_equal(
names(output_19148),
c("Target Column", "Prediction Column", "ID Column", "Family", "Classes",
"Positive Class", "Cutoff", "Target Summary", "Probability Summary",
"Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_19148$`Target Column`,
"diagnosis"
)
expect_equal(
output_19148$`Prediction Column`,
"prediction"
)
expect_equal(
output_19148$`ID Column`,
NULL
)
expect_equal(
output_19148$Family,
"Binomial"
)
expect_equal(
output_19148$Classes,
c("0", "1")
)
expect_equal(
output_19148$`Positive Class`,
"1"
)
expect_equal(
output_19148$Cutoff,
0.5
)
expect_equal(
output_19148$`Target Summary`,
list(Total = 30L,
`Class Counts` = c(`0` = 12, `1` = 18))
)
expect_equal(
output_19148$`Probability Summary`,
list(Mean = 0.509552392472203,
Median = 0.44190666731447,
Range = c(0.0133903331588954,0.991906094830483),
SD = 0.295280025751536,
IQR = 0.490849027526565)
)
expect_true(
output_19148$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'binom_baseline_reval$Process[[1]]' ####
## Testing 'as.character(binom_baseline_reval$Process[[1]])' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(binom_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: diagnosis\nPr",
"ediction column: prediction\nFamily / type: Binomial\nClasse",
"s: 0, 1\nPositive class: 1\nCutoff: 0.5\nProbabilities are o",
"f class: 1\nProbabilities < 0.5 are considered: 0\nProbabili",
"ties >= 0.5 are considered: 1\nLocale used when sorting clas",
"s levels (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\nTarget counts: total=30, 0=12, 1=1",
"8\nProbability summary: mean: 0.51, median: 0.442, range: [0",
".013, 0.992], SD: 0.295, IQR: 0.491\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(binom_baseline_reval$Process[[1]])' ####
})
test_that("gaussian evaluations are correct in baseline()", {
# We're too near the test time limit on CRAN
# So skipping this and relying on local and github actions
# to test this
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
n = 10,
family = "gaussian",
metrics = "all",
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
23.548673, 22.631836, 2.151282, 1.774305,
27.814396, 20.671713, 0.0, 0.0, 23.108507
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
20.601647, 20.021429, 1.559585, 1.191468,
23.645833, 18.214286, 0.0, 0.0, 20.370370
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
80.88782, 73.28142, 31.21325, 46.54117,
125.03483, 40.63481, 0.0, 0.0, 156.22826
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(
gaussian_baseline_summ$r2m,
rep(0.0, 9)
)
expect_equal(
gaussian_baseline_summ$r2c,
rep(0.0, 9)
)
expect_equal(gaussian_baseline_summ$AICc,
c(
83.38409, 75.63857, 29.84876, 45.02450,
126.12574, 46.63481, 0.0, 0.0, 157.02826
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
81.27241, 73.52992, 31.93310, 47.55239,
126.31295, 39.85369, 0.0, 0.0, 158.0090
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
26.47565, 22.39047, 20.67171, 24.70268, 23.08974,
22.57248, 22.69119, 22.51107, 22.56734, 27.81440
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
22.63333, 19.83333, 18.21429, 21.38889, 20.35714,
19.97619, 20.06667, 19.92857, 19.97222, 23.64583
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
40.63481, 96.54162, 63.62098, 46.39313, 57.93568,
123.76021, 82.94187, 125.03483, 108.86128, 63.15374
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4085.043, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
expect_equal(
colnames(all_coeffs),
c(
"Repetition", "term", "estimate", "std.error", "conf.level",
"conf.low", "conf.high", "statistic", "df.error", "p.value"
)
)
expect_equal(all_coeffs$Repetition, 1:10)
expect_equal(all_coeffs$estimate,
c(
28.40000, 36.00000, 41.71429, 31.33333, 34.42857,
35.57143, 35.30000, 35.71429, 35.58333, 26.37500
),
tolerance = 1e-3
)
expect_equal(all_coeffs$std.error,
c(
4.718050, 3.448759, 6.985408, 3.702852, 4.654016,
4.834303, 4.176788, 5.059458, 5.762284, 3.688484
),
tolerance = 1e-3
)
expect_equal(all_coeffs$p.value,
c(
3.836776e-03, 4.806737e-07, 9.888062e-04, 3.785030e-04,
3.133274e-04, 5.518161e-06, 1.423837e-05, 8.556549e-06,
6.954980e-05, 1.852572e-04
),
tolerance = 1e-3
)
# Test first process
## Testing 'gaussian_baseline_reval$Process[[1]]' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_11521 <- gaussian_baseline_reval$Process[[1]]
# Testing class
expect_equal(
class(output_11521),
"process_info_gaussian",
fixed = TRUE)
# Testing type
expect_type(
output_11521,
type = "list")
# Testing names
expect_equal(
names(output_11521),
c("Target Column", "Prediction Column", "ID Column", "Family", "Target Summary",
"Prediction Summary", "Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_11521$`Target Column`,
"score"
)
expect_equal(
output_11521$`Prediction Column`,
"prediction"
)
expect_equal(
output_11521$`ID Column`,
NULL
)
expect_equal(
output_11521$Family,
"Gaussian"
)
expect_equal(
output_11521$`Target Summary`,
list(Mean = 45.3333333333333,
Median = 51.5,
Range = c(11, 78),
SD = 21.2574407650307,
IQR = 29.25)
)
expect_equal(
output_11521$`Prediction Summary`,
list(Mean = 28.4,
Median = 28.4,
Range = c(28.4, 28.4),
SD = 0,
IQR = 0)
)
expect_true(
output_11521$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'gaussian_baseline_reval$Process[[1]]' ####
## Testing 'as.character(gaussian_baseline_reval$Process...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(gaussian_baseline_reval$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: score\nPredic",
"tion column: prediction\nFamily / type: Gaussian\nTarget sum",
"mary: mean: 45.333, median: 51.5, range: [11, 78], SD: 21.25",
"7, IQR: 29.25\nPrediction summary: mean: 28.4, median: 28.4,",
" range: [28.4, 28.4], SD: 0, IQR: 0\nLocale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(gaussian_baseline_reval$Process...' ####
})
test_that("gaussian evaluations of random effects models are correct in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline_random_effects <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
random_effects = "( 1 | session )",
n = 10,
family = "gaussian",
metrics = "all",
REML = TRUE, # Usually FALSE, but TRUE to avoid changing the tests
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline_random_effects$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline_random_effects$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
19.600875, 19.568833, 3.010504, 5.332666,
23.631064, 15.792418, 0.0, 0.0, 18.066222
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
17.231620, 17.259500, 2.284422, 3.206596,
20.680264, 14.117023, 0.0, 0.0, 16.173775
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
74.72007, 68.53657, 29.06934, 44.68871,
115.91591, 37.04749, 0.0, 0.0, 146.98744
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2m,
rep(0.0, 9),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2c,
c(
0.5689284, 0.5931160, 0.1248541, 0.1792077,
0.7194042, 0.3450947, 0.0, 0.0, 0.5092992
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AICc,
c(
82.00007, 74.53657, 24.19749, 39.18656,
118.31591, 54.54786, 0.0, 0.0, 148.70173
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
75.2969607911245, 68.9093092222065, 30.1532384455176, 46.2055462747716,
117.833083604798, 35.875805418616, 0, 0, 149.658560139426
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
23.3023673893311, 20.2831211351759, 18.4653958494823, 23.6310640067372,
19.6708765288482, 15.7924182217171, 19.4667891710134, 16.5250914306427,
15.8422216462345, 23.0294037571561
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
19.9254197349807, 18.1852507167284, 17.0487074806514, 20.6802643236492,
17.0551245625476, 14.2061291677316, 17.4638756853949, 14.8688972771474,
14.1170229613728, 18.7655103243966
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
37.0474916813137, 92.0001975699801, 59.3199306240758, 42.5478613478296,
51.7112998206263, 112.962482318462, 77.7532020941892, 115.915911615952,
99.9577092947807, 57.9846047242755
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Random,
rep("(1|session)", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4030.024, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
## Testing 'all_coeffs' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_coeffs),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
all_coeffs[["Repetition"]],
c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
all_coeffs[["term"]],
c("(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)"),
fixed = TRUE)
expect_equal(
all_coeffs[["estimate"]],
c(29.44832, 8.66773, 8.1377, 34.03974, 8.18275, 9.56568, 40.93218,
11.24335, 15.48873, 29.70897, 7.25112, 6.08707, 31.63809, 11.08484,
6.92283, 37.72826, 16.93924, 11.13578, 34.17823, 9.41796, 10.05082,
35.95068, 15.69245, 12.82093, 36.3069, 17.50155, 12.76391, 27.2041,
9.30371, 6.58837),
tolerance = 1e-4)
expect_equal(
all_coeffs[["std.error"]],
c(7.15631722929513, 8.48017078596171, 3.32220364038992, 5.53120433851552,
5.6642976362218, 2.25389751408461, 8.97275331112187, 11.3857986359196,
5.42155778061848, 4.96158288393548, 5.24814564894732, 2.47708793486519,
7.05688044109226, 6.80599557763085, 2.44460604383921, 10.2738954220409,
9.3566019868576, 2.37405616559033, 6.56119896982287, 7.00187724900024,
2.67291574649631, 9.73185625725407, 9.07113095976679, 2.73321585096119,
10.7798459438635, 9.97743399907886, 3.00837168723259, 5.86883171896131,
5.58366565229798, 2.08319768342238),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.level"]],
c(0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.low"]],
c(-1.34282744691279, 1.27386538040635, 3.65595044257592, 21.5272884598991,
2.10706150007808, 6.02772189002962, 16.0198275271794, 1.54493287924856,
7.79951776760785, 13.9189969554157, 1.75521843225081, 2.74168881162613,
12.0450475999266, 3.32735374149643, 3.4650146393119, 15.1155676968666,
5.73749350704645, 7.33249168749023, 18.6634644982617, 2.19342388622354,
5.96805635026065, 14.5310088047755, 5.0541486113636, 8.44222372952845,
11.9211926362553, 5.72553547803716, 8.04194093825517, 12.1177908826813,
2.86948523148235, 3.54513955526967),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.high"]],
c(60.2394682762468, 58.9776840264608, 18.1135448112533, 46.5521954864459,
31.7776340871192, 15.1802497890285, 65.8445415488026, 81.8241896481276,
30.7584161713846, 45.4989391900944, 29.9556977953848, 13.5144521340807,
51.2311299171999, 36.9283411378221, 13.8312651699865, 60.3409504173574,
50.0110277516517, 16.9117984896506, 49.6930049053992, 40.4381415401269,
16.9266142042435, 57.3703512094978, 48.7229715372203, 19.4707153600945,
60.6926040677994, 53.49790912956, 20.2584602694038, 42.2904153090177,
30.1653776868507, 12.2439623269154),
tolerance = 1e-4)
expect_equal(
all_coeffs[["statistic"]],
c(4.11501, NA, NA, 6.15413, NA, NA, 4.56183, NA, NA, 5.9878, NA,
NA, 4.4833, NA, NA, 3.67224, NA, NA, 5.20914, NA, NA, 3.69412,
NA, NA, 3.36803, NA, NA, 4.63535, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["df.error"]],
c(2, NA, NA, 9, NA, NA, 4, NA, NA, 3, NA, NA, 4, NA, NA, 11, NA,
NA, 7, NA, NA, 11, NA, NA, 9, NA, NA, 5, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["p.value"]],
c(0.05429, NA, NA, 0.00017, NA, NA, 0.01032, NA, NA, 0.00933, NA,
NA, 0.01096, NA, NA, 0.00368, NA, NA, 0.00124, NA, NA, 0.00354,
NA, NA, 0.00828, NA, NA, 0.00566, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["effect"]],
c("fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random", "fixed", "random", "random", "fixed", "random",
"random", "fixed", "random", "random", "fixed", "random", "random",
"fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random"),
fixed = TRUE)
expect_equal(
all_coeffs[["group"]],
c("", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual",
"", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_coeffs),
c("Repetition", "term", "estimate", "std.error", "conf.level", "conf.low",
"conf.high", "statistic", "df.error", "p.value", "effect", "group"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_coeffs),
c("integer", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "integer", "numeric", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_coeffs),
c("integer", "character", "double", "double", "double", "double",
"double", "double", "integer", "double", "character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_coeffs),
c(30L, 12L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_coeffs)),
character(0),
fixed = TRUE)
## Finished testing 'all_coeffs' ####
})
test_that("gaussian evaluations of random effects models are correct with REML FALSE in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(2)
# set.seed(1)
dat <- groupdata2::partition(participant.scores, p = 0.6, list_out = TRUE)
train_data <- dat[[1]]
test_data <- dat[[2]]
suppressWarnings(gaussian_baseline_random_effects <- baseline(
test_data = test_data,
train_data = train_data,
dependent_col = "score",
random_effects = "( 1 | session )",
n = 10,
family = "gaussian",
REML = FALSE,
metrics = "all",
parallel = FALSE
))
gaussian_baseline_summ <- gaussian_baseline_random_effects$summarized_metrics
gaussian_baseline_reval <- gaussian_baseline_random_effects$random_evaluations
expect_equal(
colnames(gaussian_baseline_summ),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows")
)
expect_equal(
colnames(gaussian_baseline_reval),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random")
)
# Summarized results
expect_equal(gaussian_baseline_summ$Measure, c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "All_rows"
))
expect_equal(gaussian_baseline_summ$RMSE,
c(
19.9461643038004, 19.8243992085347, 3.16669902505845, 5.45212618245324,
24.498950920267, 15.8922827970791, 0, 0, 18.2579833481909
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$MAE,
c(
17.5688071767949, 17.5664123038964, 2.36581521783252, 3.32573044194396,
20.960459513901, 14.4129406656979, 0, 0, 16.3907116308631
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AIC,
c(
80.3465725153843, 74.2525560821578, 29.4337002888526, 45.288244994372,
122.097050951734, 42.4261074987794, 0, 0, 152.799138513963
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$`Training Rows`,
c(9.50, 9.0, 3.341656, 5.0, 14.0, 5.0, 0.0, 0.0, 18.0),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2m,
rep(0.0, 9),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$r2c,
c(
0.43613229083168, 0.474672230635666, 0.149845112686991, 0.23376511982053,
0.624606985762511, 0.200718929950735, 0, 0, 0.397952267974043
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$AICc,
c(
87.6265725153843, 80.2525560821578, 24.5752651300548, 39.9130171335876,
124.497050951734, 59.3905521226883, 0, 0, 154.513424228249
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_summ$BIC,
c(
80.9234641973603, 74.6252989452318, 30.5156235782933, 46.8050859521017,
124.01422294058, 41.2544212360817, 0, 0, 155.470253787652
),
tolerance = 1e-3
)
# The random evaluations
expect_equal(gaussian_baseline_reval$RMSE,
c(
24.498950920267, 20.5418159984439, 18.992695846541, 23.6559983524106,
19.8835818741749, 15.8922827970791, 19.7652165428946, 16.7141367039409,
16.0496991056741, 23.4672648965776
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$MAE,
c(
20.960459513901, 18.4479900242695, 17.3968470787666, 20.6756807573846,
17.3014038386473, 14.4129406656979, 17.7359775290262, 15.1341822802114,
14.4362959391151, 19.1862941409292
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$AIC,
c(
42.4261074987794, 97.0575891839717, 65.3436873296684, 47.3905521226883,
57.2591370177335, 119.245364275572, 83.1614248346471, 122.097050951734,
106.339403446441, 63.1454084926068
),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$`Training Rows`,
c(5, 12, 7, 6, 7, 14, 10, 14, 12, 8),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Dependent,
rep("score", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Fixed,
rep("1", 10),
tolerance = 1e-3
)
expect_equal(gaussian_baseline_reval$Random,
rep("(1|session)", 10),
tolerance = 1e-3
)
all_predictions <- dplyr::bind_rows(gaussian_baseline_reval$Predictions)
expect_equal(
colnames(all_predictions),
c("Repetition", "Target", "Prediction")
)
expect_equal(
sum(as.numeric(all_predictions$Repetition)),
sum(rep(1:10, each = 12))
)
expect_equal(sum(all_predictions$Target), 5440)
expect_equal(sum(all_predictions$Prediction), 4042.804, tolerance = 1e-3)
all_coeffs <- dplyr::bind_rows(gaussian_baseline_reval$Coefficients)
## Testing 'all_coeffs' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(all_coeffs),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
all_coeffs[["Repetition"]],
c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7,
8, 8, 8, 9, 9, 9, 10, 10, 10),
tolerance = 1e-4)
expect_equal(
all_coeffs[["term"]],
c("(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)", "(Intercept)", "SD (Intercept)",
"SD (Observations)", "(Intercept)", "SD (Intercept)", "SD (Observations)",
"(Intercept)", "SD (Intercept)", "SD (Observations)", "(Intercept)",
"SD (Intercept)", "SD (Observations)"),
fixed = TRUE)
expect_equal(
all_coeffs[["estimate"]],
c(29.04756, 4.78805, 8.17091, 34.32034, 6.2161, 9.52228, 41.24164,
7.6352, 15.23618, 29.94422, 5.55015, 5.98193, 31.78646, 8.81967,
6.83742, 37.66581, 13.50784, 11.12957, 34.3378, 7.2613, 9.92882,
35.96226, 12.45905, 12.79418, 36.2792, 13.87662, 12.74618, 27.11524,
7.22274, 6.58729),
tolerance = 1e-4)
expect_equal(
all_coeffs[["std.error"]],
c(5.00667887999703, 5.67371825188572, 3.32263054737512, 4.58125952668684,
4.19962069675716, 2.23902846895305, 7.4157534194802, 9.40523002404368,
5.22228218871595, 4.11850585776279, 3.92278892473836, 2.40582757012123,
5.86297959144672, 4.82362962007409, 2.40174657096886, 8.40640786916794,
6.41710541301027, 2.37215499333295, 5.47687326594097, 4.98399903152422,
2.61097972219224, 8.01516995843001, 6.33568334015868, 2.72644466961516,
8.84393766439164, 6.93470544419571, 3.00359441565683, 4.79166391324045,
3.95574719227331, 2.08166583997345),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.level"]],
c(0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95,
0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95, 0.95),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.low"]],
c(7.5055635091569, 0.46936052606292, 3.68244920326646, 23.9568134523162,
1.65364927534353, 6.00610431660554, 20.6522095649089, 0.682793897696677,
7.78255751654273, 16.8372929040608, 1.38894433687881, 2.71959079455526,
15.5082176576566, 3.01936677556473, 3.43474719144677, 19.1634263807258,
5.3236524900518, 7.32915051912752, 21.3870490121703, 1.89133471569308,
5.93003670321845, 18.3209851742201, 4.59865828520289, 8.42599036674567,
16.2728226558001, 5.21079259307738, 8.03150734947051, 14.7978731028873,
2.46897286078753, 3.54581770261687),
tolerance = 1e-4)
expect_equal(
all_coeffs[["conf.high"]],
c(50.5895646091533, 48.8439653196076, 18.1302694700696, 44.6838715581805,
23.366460023273, 15.0969356177559, 61.8310741306481, 85.3790886729535,
29.8284029460688, 43.0511404047138, 22.17813974328, 13.1576934421859,
48.0646996347502, 25.7625665742604, 13.6109854945425, 56.1681843201583,
34.2737942870217, 16.9006519912674, 47.2885437072587, 27.8779371562027,
16.6240922819864, 53.6035254424537, 33.7550247871271, 19.426912395414,
56.2855765254888, 36.9541620366811, 20.2284566698823, 39.4326015385872,
21.1294251635629, 12.2376231801648),
tolerance = 1e-4)
expect_equal(
all_coeffs[["statistic"]],
c(5.80176, NA, NA, 7.49146, NA, NA, 5.56136, NA, NA, 7.27065, NA,
NA, 5.42155, NA, NA, 4.48061, NA, NA, 6.2696, NA, NA, 4.48677,
NA, NA, 4.10215, NA, NA, 5.65884, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["df.error"]],
c(2, NA, NA, 9, NA, NA, 4, NA, NA, 3, NA, NA, 4, NA, NA, 11, NA,
NA, 7, NA, NA, 11, NA, NA, 9, NA, NA, 5, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["p.value"]],
c(0.02845, NA, NA, 4e-05, NA, NA, 0.00512, NA, NA, 0.00537, NA,
NA, 0.00561, NA, NA, 0.00093, NA, NA, 0.00042, NA, NA, 0.00092,
NA, NA, 0.00267, NA, NA, 0.0024, NA, NA),
tolerance = 1e-4)
expect_equal(
all_coeffs[["effect"]],
c("fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random", "fixed", "random", "random", "fixed", "random",
"random", "fixed", "random", "random", "fixed", "random", "random",
"fixed", "random", "random", "fixed", "random", "random", "fixed",
"random", "random"),
fixed = TRUE)
expect_equal(
all_coeffs[["group"]],
c("", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual",
"", "session", "Residual", "", "session", "Residual", "", "session",
"Residual", "", "session", "Residual", "", "session", "Residual"),
fixed = TRUE)
# Testing column names
expect_equal(
names(all_coeffs),
c("Repetition", "term", "estimate", "std.error", "conf.level", "conf.low",
"conf.high", "statistic", "df.error", "p.value", "effect", "group"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(all_coeffs),
c("integer", "character", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "integer", "numeric", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(all_coeffs),
c("integer", "character", "double", "double", "double", "double",
"double", "double", "integer", "double", "character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(all_coeffs),
c(30L, 12L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(all_coeffs)),
character(0),
fixed = TRUE)
## Finished testing 'all_coeffs' ####
})
# TODO Create baseline test where both classes are 50% 50% , 100% 0%, 0% 100%, 30/70 etc.
# Do we get what we expect?
test_that("multinomial evaluations are correct in baseline()", {
testthat::skip_on_cran()
xpectr::set_test_seed(1)
targets_1 <- dplyr::sample_n(tibble::enframe(rep(1:3, each = 10), value = "targets_3"), 25) %>%
dplyr::select(-"name")
targets_2 <- dplyr::sample_n(tibble::enframe(rep(1:4, each = 10), value = "targets_4"), 25) %>%
dplyr::select(-"name")
targets_3 <- dplyr::sample_n(tibble::enframe(rep(1:5, each = 10), value = "targets_5"), 25) %>%
dplyr::select(-"name")
different_targets <- targets_1 %>%
dplyr::bind_cols(targets_2, targets_3)
multinom_baseline <- baseline(
test_data = different_targets,
dependent_col = "targets_3",
n = 10,
family = "multinomial",
metrics = list(
"Weighted Accuracy" = TRUE,
"Accuracy" = FALSE,
"Specificity" = FALSE,
"AUC" = TRUE
),
parallel = FALSE
)
multinom_baseline_summ <- multinom_baseline$summarized_metrics
multinom_baseline_class <- tidyr::unnest(multinom_baseline$summarized_class_level_results, "Results")
multinom_baseline_random_eval_summ <- multinom_baseline$random_evaluations
multinom_baseline_random_eval_class <- dplyr::bind_rows(multinom_baseline$random_evaluations$`Class Level Results`)
# Summarized results
if (TRUE) {
# # Summarized results
expect_equal(
colnames(multinom_baseline_summ),
c(
"Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Accuracy", "F1", "Sensitivity",
"Pos Pred Value", "Neg Pred Value", "AUC",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence"
)
)
expect_equal(
multinom_baseline_summ$Measure,
c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "CL_Max", "CL_Min",
"CL_NAs", "CL_INFs", "All_1", "All_2", "All_3"
)
)
expect_equal(multinom_baseline_summ$`Balanced Accuracy`,
c(
0.567046957671958, 0.566798941798942, 0.0945059449777177, 0.118138227513228,
0.73776455026455, 0.427744708994709, 0, 0,
0.845238095238095, 0.349206349206349, 0, 0, 0.5, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted Accuracy`,
c(
0.61312, 0.616, 0.0844143852932925, 0.1224, 0.7536, 0.4976, 0, 0, NA, NA,
NA, NA, 0.5616, 0.5392, 0.5616
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$F1,
c(
0.414096213384139, 0.412686127082412, 0.126458068440281, 0.171577755033637,
0.638304093567251, 0.237745098039216, 0, 0,
0.75, 0.125, 0, 0, NA, NA, NA
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Sensitivity,
c(
0.422751322751323, 0.420634920634921, 0.126356719688141, 0.149470899470899,
0.656084656084656, 0.232804232804233, 0, 0,
0.857142857142857, 0.111111111111111, 0, 0, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Pos Pred Value`,
c(
0.421105098605099, 0.424404761904762, 0.130757940506255, 0.18994708994709,
0.644444444444444, 0.24537037037037, 0, 0,
0.714285714285714, 0.111111111111111, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Neg Pred Value`,
c(
0.712455474092471, 0.710389433551198, 0.0639629682811759, 0.0885480929888824,
0.824780701754386, 0.620098039215686, 0, 0,
0.9375, 0.538461538461539, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$AUC,
c(
0.557671957671958, 0.546443268665491, 0.107836177836045, 0.127572016460905,
0.766901822457378, 0.397119341563786, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Kappa,
c(
0.132055158549256, 0.134473657323828, 0.187086327373864, 0.243825884877481,
0.462128481918942, -0.1375345264999, 0, 0,
0.635036496350365, -0.277372262773723, 0, 0, 0, 0, 0
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$MCC,
c(0.135653399205696, 0.136605182450698, 0.190316058026005, 0.255168588966898,
0.469598884822668, -0.137349796341982, 0, 0, NA, NA, NA, NA,
0, 0, 0),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Detection Rate`,
c(
0.14, 0.14, 0.0422806613120899, 0.0566666666666667,
0.213333333333333, 0.08, 0, 0,
0.24, 0.04, 0, 0, 0.12, 0.0933333333333333, 0.12
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 3.70074341541719e-17,
5.55111512312578e-17, 0.333333333333333, 0.333333333333333, 0,
0, 0.48, 0.16, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Prevalence,
c(
0.333333333333333, 0.333333333333333, 0, 0, 0.333333333333333,
0.333333333333333, 0, 0, 0.36, 0.28, 0,
0, 0.333333333333333, 0.333333333333333, 0.333333333333333
),
tolerance = 1e-3
)
}
# Summarized class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_class),
c(
"Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Pos Pred Value", "Neg Pred Value", "Kappa", "Detection Rate",
"Detection Prevalence", "Prevalence"
)
)
expect_equal(
multinom_baseline_class$Class,
as.character(rep(1:3, each = 10))
)
expect_equal(
multinom_baseline_class$Measure,
rep(c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0", "All_1"), 3)
)
expect_equal(sum(multinom_baseline_class$`Balanced Accuracy`), 10.58557, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Accuracy, na.rm = TRUE), 11.01848, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$F1, na.rm = TRUE), 7.506455, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Sensitivity), 9.202117, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Pos Pred Value`, na.rm = T), 7.107812, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Neg Pred Value`, na.rm = T), 11.06547, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Kappa), 3.139021, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Rate`), 3.067856, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Prevalence`), 7.413783, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Prevalence), 6)
}
# Random evaluations results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_summ),
c(
"Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Accuracy",
"F1", "Sensitivity", "Pos Pred Value", "Neg Pred Value", "AUC",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "Predictions", "ROC", "Confusion Matrix", "Class Level Results",
"Process", "Dependent"
)
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[2]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$Predictions[[1]]),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
multinom_baseline_random_eval_summ$Repetition,
1:10
)
expect_equal(multinom_baseline_random_eval_summ$`Overall Accuracy`,
c(0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44, 0.40, 0.32, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Balanced Accuracy`,
c(
0.6651786, 0.5239749, 0.7377646, 0.4661045, 0.6362434,
0.4277447, 0.5806878, 0.5529101, 0.4980159, 0.5818452
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Accuracy`,
c(
0.7088, 0.5744, 0.7536, 0.5168, 0.68, 0.4976, 0.6288, 0.6032,
0.536, 0.632
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$F1,
c(
0.5511905, 0.3472767, 0.6383041, 0.2769608, 0.5111111,
0.2377451, 0.4398148, 0.4019608, 0.3131868, 0.4234115
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Sensitivity,
c(
0.5502646, 0.3650794, 0.6560847, 0.2910053, 0.5132275,
0.2328042, 0.4391534, 0.4021164, 0.3386243, 0.4391534
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Pos Pred Value`,
c(
0.5627706, 0.3407407, 0.6444444, 0.2726190, 0.5317460,
0.2453704, 0.4497354, 0.4154762, 0.3148148, 0.4333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Neg Pred Value`,
c(
0.7804233, 0.6874269, 0.8247807, 0.6453159, 0.7612836,
0.6200980, 0.7199074, 0.7008715, 0.6566697, 0.7277778
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$AUC,
c(
0.666960611405056, 0.529100529100529, 0.766901822457378, 0.496766607877719,
0.563786008230453, 0.397119341563786, 0.6222810111699, 0.481187536743092,
0.468841857730747, 0.583774250440917
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Kappa,
c(
0.33221743, 0.04113827, 0.46212848, -0.07364408,
0.27425438, -0.13753453, 0.16320289, 0.10883768,
-0.01015859, 0.16010964
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$MCC,
c(0.339040525810531, 0.0486630968728153, 0.469598884822668, -0.0726394381146181,
0.281551008833778, -0.137349796341982, 0.164251207729469, 0.108959157171927,
-0.0175562702416747, 0.172015615514047),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Rate`,
c(
0.18666667, 0.12000000, 0.21333333, 0.09333333,
0.17333333, 0.08000000, 0.14666667, 0.13333333,
0.10666667, 0.14666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Prevalence`,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Prevalence,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$Dependent,
rep("targets_3", 10)
)
expect_equal(
length(multinom_baseline_random_eval_summ$Predictions),
10
)
expect_equal(
length(multinom_baseline_random_eval_summ$`Confusion Matrix`),
10
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$Target,
as.character(c(1, 2, 2, 3, 1, 3, 3, 2, 2, 1, 1, 1, 2, 1, 2, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3))
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$`Predicted Class`,
c(
"2", "1", "2", "3", "1", "2", "3", "3", "2", "1", "3", "1",
"3", "3", "2", "2", "3", "3", "1", "3", "1", "3", "3", "1", "2"
)
)
preds <- dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions[[1]]$Prediction)
expect_equal(preds$`1`,
c(
0.3628243, 0.3609037, 0.2858221, 0.3747737, 0.3817009,
0.2737241, 0.3419418, 0.3049587, 0.2962721, 0.4240434,
0.2931166, 0.4357583, 0.2827270, 0.2248048, 0.1981635,
0.2229453, 0.3005942, 0.3278586, 0.4148558, 0.3384068,
0.4079048, 0.3290621, 0.3476237, 0.4361450, 0.3350177
),
tolerance = 1e-4
)
expect_equal(preds$`2`,
c(
0.3857806, 0.3426045, 0.4230804, 0.2251754, 0.3129899,
0.4752175, 0.2249170, 0.3047626, 0.4820548, 0.2148414,
0.3443212, 0.3331387, 0.2928041, 0.3148037, 0.4530263,
0.4719141, 0.3380788, 0.2966119, 0.2536526, 0.3053621,
0.2405719, 0.3336935, 0.2739201, 0.2804216, 0.3779097
),
tolerance = 1e-4
)
expect_equal(preds$`3`,
c(
0.2513951, 0.2964917, 0.2910974, 0.4000508, 0.3053092,
0.2510584, 0.4331413, 0.3902787, 0.2216731, 0.3611152,
0.3625622, 0.2311030, 0.4244689, 0.4603915, 0.3488102,
0.3051406, 0.3613270, 0.3755294, 0.3314917, 0.3562311,
0.3515233, 0.3372444, 0.3784562, 0.2834334, 0.2870727
),
tolerance = 1e-4
)
all_preds <- dplyr::bind_rows(
dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions)$Prediction
)
expect_equal(
all_preds$`1`,
c(
0.362824302965311, 0.360903717826462, 0.285822146515266, 0.37477371815703,
0.381700937144022, 0.273724077239358, 0.341941761553692, 0.304958692149421,
0.296272111439159, 0.42404336282095, 0.293116571086624, 0.435758286831625,
0.28272698563188, 0.224804797891925, 0.198163481343272, 0.222945305755905,
0.300594211265473, 0.3278586388128, 0.414855759724269, 0.33840681107911,
0.407904832070239, 0.329062099428566, 0.347623656119297, 0.436145034077774,
0.335017655729982, 0.331364482540279, 0.283536882101457, 0.281143446843595,
0.425995399790343, 0.261537001684637, 0.237313955938272, 0.23261151589263,
0.385748271928449, 0.334411930525475, 0.401249217427923, 0.420235849517611,
0.425289710559998, 0.262465444718002, 0.336697104880243, 0.231858151646129,
0.210092588546093, 0.47741912516683, 0.244565586519055, 0.286947978160956,
0.417209987822813, 0.409276670773936, 0.354307298336307, 0.276564090289706,
0.330862775865476, 0.44695225847507, 0.317576338796576, 0.382847300413345,
0.224081474342672, 0.308365835600141, 0.302015399930661, 0.313332459936172,
0.251934064646274, 0.23915184388103, 0.277538091890923, 0.471891254622384,
0.40754972988395, 0.345372198209239, 0.33466402327811, 0.462461145964111,
0.266579885476856, 0.352899631920573, 0.421312945065338, 0.200478726356666,
0.309794998292503, 0.380501567619241, 0.370761060364264, 0.286594888362904,
0.361067815387182, 0.245947918320429, 0.389260130880553, 0.321714471097802,
0.331089372813525, 0.256210536793289, 0.285625126260833, 0.27568485403864,
0.274822151212381, 0.389484563107126, 0.212979233666992, 0.296561920614094,
0.200225006332582, 0.242554965054235, 0.401234678799669, 0.330009524801572,
0.348663184860405, 0.411214822720279, 0.307151655834041, 0.204995757155556,
0.351402327921561, 0.374094844465352, 0.248711786514304, 0.336708510840726,
0.408292124109811, 0.357562258085327, 0.34181138284562, 0.323252856286123,
0.447544862966328, 0.429915527750429, 0.363601070683015, 0.333552425710284,
0.398225671426714, 0.221696833964976, 0.271265251552504, 0.481294916218702,
0.328149707350169, 0.450855293083027, 0.237104558776676, 0.374383211394644,
0.443948913175648, 0.494051191743219, 0.256273986260961, 0.489484795353784,
0.329464982964633, 0.220032486448317, 0.408557119092641, 0.319230850919545,
0.355237403525459, 0.23455484947749, 0.382020061725786, 0.267110221515588,
0.449602783872994, 0.202164855975727, 0.236445026776993, 0.319748604954957,
0.293320401651886, 0.287711207487059, 0.358419477510523, 0.346556723204159,
0.206074092549024, 0.22717751457188, 0.397646700390622, 0.446688182141175,
0.25620775941098, 0.501804532497241, 0.408063047760274, 0.443251289188255,
0.249444482951462, 0.383829461406706, 0.526313287884649, 0.452849242524878,
0.253013631607335, 0.334191437619488, 0.210413611589952, 0.23421023559276,
0.321507174883409, 0.509517153778936, 0.315671789411075, 0.255620649439559,
0.239331881508991, 0.322754669547894, 0.435042212296461, 0.251412865189562,
0.282387104005381, 0.245973141871465, 0.341257410449456, 0.277527088361948,
0.399220690874693, 0.439926753580167, 0.345819178673885, 0.305382501740128,
0.418275547531307, 0.357628745014896, 0.294197703135495, 0.438696916274104,
0.316517159370203, 0.357792733303972, 0.28831445694109, 0.327860691995961,
0.22281288859293, 0.314065020723387, 0.459321941470143, 0.387693577991577,
0.363268586267414, 0.303950826699894, 0.372655080723904, 0.339529039157154,
0.224621024989129, 0.296482850978123, 0.314332611351849, 0.234267899937577,
0.437859055024521, 0.269731707168111, 0.331690324355338, 0.280473038011755,
0.399824753263675, 0.383719422449624, 0.330850303521819, 0.225277208971775,
0.359553396121816, 0.328615974470287, 0.231945476475709, 0.214223736176299,
0.20582242433687, 0.375727645283227, 0.235524783117192, 0.286268463291008,
0.229673192858615, 0.283772599798294, 0.219611172339202, 0.295826542146669,
0.285141592656138, 0.192558055771125, 0.355721326100929, 0.409545410065343,
0.331835694337497, 0.230954644322117, 0.340582165139984, 0.294251623668547,
0.298768624646445, 0.291985965689547, 0.300172335564108, 0.318523203157749,
0.237566842343213, 0.37261470439223, 0.331390765035546, 0.364342409833174,
0.397916841690445, 0.217399738068907, 0.264193767365295, 0.208427577921687,
0.420006306243977, 0.292308232373888, 0.281443833338, 0.30947189334135,
0.257203806304276, 0.295150877610198, 0.439630183471502, 0.321730635029117,
0.383315062595601, 0.225583239939362, 0.309872283286329, 0.255887220229597,
0.316939046835727, 0.449868348167623, 0.304614105760487, 0.248742717566446,
0.305293159179038, 0.222558427746317, 0.244342581208601, 0.503534783305527,
0.286383868562386, 0.428804177911175, 0.295404759872436, 0.30687255200497,
0.445596166797302, 0.334706962656109
)
)
expect_equal(
all_preds$`2`,
c(
0.385780580440307, 0.342604539124775, 0.423080439134247, 0.225175445397002,
0.312989908016585, 0.475217543730522, 0.224916968457944, 0.30476258690942,
0.482054751251862, 0.214841434476723, 0.344321198446437, 0.333138708845406,
0.292804142075078, 0.31480368331023, 0.453026287138725, 0.4719140606185,
0.338078757628025, 0.296611945042962, 0.25365258932653, 0.305362051955673,
0.240571861737913, 0.333693522274202, 0.27392009695986, 0.28042156343828,
0.377909676645575, 0.279959262495121, 0.2122887561223, 0.470196696415608,
0.239862567097843, 0.370221172919109, 0.25443866213747, 0.477173210501813,
0.259642635838842, 0.322066433258374, 0.353453695410528, 0.1765751153703,
0.285504011914052, 0.424456315084771, 0.288460995364288, 0.35958455461334,
0.478761722406255, 0.250631249168976, 0.445446891169545, 0.369385615626211,
0.349831286201674, 0.234976115878086, 0.378701320138235, 0.431201840261207,
0.350278234492975, 0.262288382369503, 0.307058606700166, 0.320533642024935,
0.43236586844692, 0.292897655407014, 0.272621596850078, 0.270996516799443,
0.410085989322878, 0.444842557489891, 0.388276130257391, 0.235166674305724,
0.285112014266039, 0.437273128065374, 0.454306056457757, 0.193441477963145,
0.420457813840421, 0.20658635492773, 0.266523286854865, 0.398245797218399,
0.397335280211415, 0.214226033921459, 0.334049449261309, 0.465337959942035,
0.233723758733514, 0.31472699114585, 0.190099685465158, 0.336264164127875,
0.228217420461845, 0.478951976081042, 0.445589110407979, 0.429453925922651,
0.472930553187392, 0.332138049773757, 0.431357175765288, 0.227158863320801,
0.414490874557254, 0.492841238109576, 0.234359938505408, 0.446090237254051,
0.35319859961771, 0.345280017404155, 0.327026905651897, 0.315397998189469,
0.341892635859671, 0.363505947910766, 0.404023366558927, 0.276862535515932,
0.294742179813213, 0.294768056642531, 0.266684517736902, 0.311820705852887,
0.274265040914617, 0.245816171241433, 0.440341510685916, 0.421104352741149,
0.388918784994157, 0.421278084276545, 0.240879703622285, 0.289683431392311,
0.339677067961078, 0.208333263474704, 0.437464387830416, 0.367535015967519,
0.226639265821188, 0.311401754535334, 0.372083534671264, 0.250419612561257,
0.299632515128755, 0.32824246642957, 0.297400161614639, 0.313227906367457,
0.300459136297168, 0.303048382688999, 0.423041978939619, 0.21995462356869,
0.220422428036373, 0.405812939731575, 0.295769536850216, 0.276168343895395,
0.265750665287833, 0.253402549071951, 0.411972235353439, 0.351927605924981,
0.485986422454677, 0.321551112338582, 0.206970975070247, 0.200850312668311,
0.372875934652805, 0.234374628487236, 0.245897425556106, 0.253770902739585,
0.468416469360073, 0.410120970528605, 0.238800068178443, 0.280904468157008,
0.359143089366176, 0.407538360613741, 0.463482198596469, 0.346052750154407,
0.287160871624961, 0.227514884062728, 0.240774210253124, 0.468578579358505,
0.353317373812137, 0.349640083059674, 0.369950508544139, 0.380002971753307,
0.289234489357681, 0.395516529035609, 0.467797098876064, 0.338410210733615,
0.331006277488809, 0.266926876246406, 0.198909961692134, 0.477956034101944,
0.304153252664601, 0.269291217911081, 0.42201242766336, 0.236603843824688,
0.323467921547914, 0.352132946072107, 0.369703528563403, 0.329596532065509,
0.327226373739197, 0.462085224932779, 0.189039991684081, 0.314951443633383,
0.284435887439821, 0.366590341963145, 0.395601714171088, 0.335225687954128,
0.5311988845767, 0.346007208388757, 0.446522267443117, 0.343863071903928,
0.292892871402239, 0.382830131827548, 0.385357786983514, 0.214142145338179,
0.376626067248013, 0.389178684389126, 0.338344548396213, 0.403001958525644,
0.290654812925342, 0.247101836432825, 0.370492901531161, 0.461281265139314,
0.375012631382266, 0.363470643270294, 0.226067381809261, 0.313047833074042,
0.479443190691509, 0.490484639496364, 0.391240933517263, 0.478047191943444,
0.356438854507902, 0.343453444225452, 0.216202187035667, 0.237739153185802,
0.301640124052444, 0.247512173039711, 0.309589249125308, 0.308972701838745,
0.358423819908425, 0.304335319964878, 0.448013834885817, 0.378099789829676,
0.541697788505534, 0.268360413749574, 0.331964563898707, 0.26728121083406,
0.225911392328278, 0.533006780442077, 0.342769074491857, 0.347630476342447,
0.345065351570056, 0.35598982747141, 0.453613405495365, 0.191197557332096,
0.401269074847496, 0.438308839883431, 0.239209391932476, 0.29631107556132,
0.178586958336824, 0.478858385133731, 0.475817691964852, 0.427658658905656,
0.298338105256703, 0.272983994354953, 0.308124320381545, 0.471773233423636,
0.373331904052012, 0.260774223314073, 0.326618966011962, 0.252950597054979,
0.399516649309411, 0.258469593402028, 0.328387994212459, 0.263157498352965,
0.180860493953652, 0.325672027376522
)
)
expect_equal(
all_preds$`3`,
c(
0.251395116594382, 0.296491743048763, 0.291097414350488, 0.400050836445967,
0.305309154839393, 0.25105837903012, 0.433141269988364, 0.390278720941159,
0.221673137308979, 0.361115202702327, 0.362562230466938, 0.231103004322969,
0.424468872293042, 0.460391518797845, 0.348810231518003, 0.305140633625595,
0.361327031106502, 0.375529416144238, 0.331491650949201, 0.356231136965217,
0.351523306191848, 0.337244378297231, 0.378456246920842, 0.283433402483946,
0.287072667624444, 0.3886762549646, 0.504174361776243, 0.248659856740797,
0.334142033111814, 0.368241825396254, 0.508247381924258, 0.290215273605557,
0.354609092232709, 0.343521636216152, 0.245297087161549, 0.403189035112089,
0.28920627752595, 0.313078240197227, 0.374841899755469, 0.408557293740532,
0.311145689047652, 0.271949625664194, 0.3099875223114, 0.343666406212833,
0.232958725975514, 0.355747213347978, 0.266991381525458, 0.292234069449087,
0.318858989641549, 0.290759359155428, 0.375365054503257, 0.29661905756172,
0.343552657210408, 0.398736508992845, 0.425363003219261, 0.415671023264385,
0.337979946030848, 0.316005598629079, 0.334185777851686, 0.292942071071892,
0.30733825585001, 0.217354673725387, 0.211029920264134, 0.344097376072744,
0.312962300682723, 0.440514013151697, 0.312163768079797, 0.401275476424934,
0.292869721496081, 0.4052723984593, 0.295189490374428, 0.24806715169506,
0.405208425879304, 0.439325090533721, 0.420640183654289, 0.342021364774323,
0.440693206724629, 0.264837487125669, 0.268785763331188, 0.294861220038709,
0.252247295600227, 0.278377387119117, 0.35566359056772, 0.476279216065105,
0.385284119110164, 0.264603796836189, 0.364405382694923, 0.223900237944377,
0.298138215521885, 0.243505159875566, 0.365821438514061, 0.479606244654974,
0.306705036218769, 0.262399207623883, 0.347264846926769, 0.386428953643342,
0.296965696076976, 0.347669685272142, 0.391504099417478, 0.364926437860989,
0.278190096119055, 0.324268301008138, 0.196057418631069, 0.245343221548567,
0.212855543579129, 0.357025081758479, 0.487855044825211, 0.229021652388988,
0.332173224688753, 0.340811443442268, 0.325431053392908, 0.258081772637838,
0.329411821003164, 0.194547053721447, 0.371642479067775, 0.260095592084959,
0.370902501906612, 0.451725047122112, 0.29404271929272, 0.367541242712998,
0.344303460177374, 0.462396767833511, 0.194937959334594, 0.512935154915721,
0.329974788090632, 0.392022204292698, 0.467785436372791, 0.404083051149648,
0.440928933060281, 0.45888624344099, 0.229608287136038, 0.30151567087086,
0.307939484996299, 0.451271373089538, 0.395382324539131, 0.352461505190514,
0.370916305936214, 0.263820839015524, 0.34603952668362, 0.30297780807216,
0.282139047688465, 0.206049568064689, 0.234886643936908, 0.266246289318114,
0.387843279026488, 0.258270201766771, 0.326104189813578, 0.419737014252832,
0.39133195349163, 0.262967962158336, 0.443554000335801, 0.275800771201936,
0.407350744678872, 0.327605247392432, 0.1950072791594, 0.36858416305713,
0.428378406636938, 0.358510329092926, 0.190945490674481, 0.384062700904437,
0.269773031636498, 0.293146370173427, 0.455270859633981, 0.216661464157928,
0.277571199804092, 0.373080037074023, 0.283789869201145, 0.324699239901208,
0.360014919081883, 0.290074320623921, 0.341982014495507, 0.34254277593853,
0.449960737667873, 0.223849754343834, 0.351638066845776, 0.29735497837504,
0.352295526292765, 0.329458831336961, 0.231743205105008, 0.325245272888717,
0.24418009043417, 0.35750994063312, 0.239145121205034, 0.421869028158495,
0.269248073573241, 0.347438161004341, 0.282951888661148, 0.505384816650066,
0.223549179488312, 0.22710189316125, 0.330805148081968, 0.371720832502581,
0.349791790952842, 0.424282189096888, 0.39756162199313, 0.324494998684386,
0.419164944280864, 0.260801711446479, 0.538407835073546, 0.400683703634949,
0.290883616449876, 0.225742760705341, 0.389147894143535, 0.226126265909887,
0.35841955283596, 0.463988500003423, 0.428076486863404, 0.352715436748856,
0.366524181610059, 0.521533182638172, 0.349828585734708, 0.396775674492708,
0.34280755544513, 0.403678714345574, 0.251813829550075, 0.303377007012575,
0.220735369151253, 0.359024881858196, 0.336644671065747, 0.368376379332766,
0.376171765981278, 0.249593481489016, 0.393037158142848, 0.443941945735865,
0.234928342185967, 0.351701940154702, 0.264942761166635, 0.499330549326554,
0.341527118848228, 0.266540282506371, 0.321160424596023, 0.381958289409563,
0.438097979067575, 0.295558374926908, 0.214310024748819, 0.316454120864747,
0.38472284790757, 0.277147657477424, 0.387261573857968, 0.279484049009918,
0.32137493676895, 0.51666734893961, 0.429038452779437, 0.243514619639494,
0.314099482128203, 0.312726228686797, 0.376207245915106, 0.429969949642065,
0.373543339249046, 0.339621009967369
)
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Prediction,
c("1", "2", "3", "1", "2", "3", "1", "2", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Target,
c("1", "1", "1", "2", "2", "2", "3", "3", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$N,
c(5, 1, 3, 2, 3, 2, 0, 3, 6)
)
# Test first 'Process'
## Testing 'multinom_baseline_random_eval_summ$Process...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Unsupported class: using fallback tests
# Assigning output
output_10358 <- multinom_baseline_random_eval_summ$Process[[1]]
# Testing class
expect_equal(
class(output_10358),
"process_info_multinomial",
fixed = TRUE)
# Testing type
expect_type(
output_10358,
type = "list")
# Testing names
expect_equal(
names(output_10358),
c("Target Column", "Prediction Columns", "ID Column", "Family",
"Classes", "Softmax Applied", "Target Summary", "Prediction Summary",
"Locale"),
fixed = TRUE)
# Testing dput() content
# NOTE: Written out manually as Locale is C in R cmd check
expect_equal(
output_10358$`Target Column`,
"targets_3"
)
expect_equal(
output_10358$`Prediction Columns`,
c("1","2", "3")
)
expect_equal(
output_10358$`ID Column`,
NULL
)
expect_equal(
output_10358$Family,
"Multinomial"
)
expect_equal(
output_10358$Classes,
c("1", "2", "3")
)
expect_equal(
output_10358$`Softmax Applied`,
FALSE
)
expect_equal(
output_10358$`Target Summary`,
list(Total = 25L,
`Class Counts` = c(`1` = 9, `2` = 7, `3` = 9))
)
expect_equal(
output_10358$`Prediction Summary`,
list(Total = 25L,
`Class Counts` = c(`1` = 7, `2` = 7, `3` = 11))
)
expect_true(
output_10358$Locale %in% c(
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()
)
)
## Finished testing 'multinom_baseline_random_eval_summ$Process...' ####
## Testing 'as.character(multinom_baseline_random_eval...' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- as.character(multinom_baseline_random_eval_summ$Process[[1]])
# Testing class
expect_equal(
class(output_19148),
"character",
fixed = TRUE)
# Testing type
expect_type(
output_19148,
type = "character")
# Testing values
expect_true(
output_19148 %in%
paste0("---\nProcess Information\n---\nTarget column: targets_3\nPr",
"ediction columns: 1, 2, 3\nFamily / type: Multinomial\nClass",
"es: 1, 2, 3\nSoftmax: Not applied\nTarget counts: total=25, ",
"1=9, 2=7, 3=9\nPrediction counts: total=25, 1=7, 2=7, 3=11\n",
"Locale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
# Testing names
expect_equal(
names(output_19148),
NULL,
fixed = TRUE)
# Testing length
expect_equal(
length(output_19148),
1L)
# Testing sum of element lengths
expect_equal(
sum(xpectr::element_lengths(output_19148)),
1L)
## Finished testing 'as.character(multinom_baseline_random_eval...' ####
}
# Random evaluations class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_class),
c(
"Repetition", "Class", "Balanced Accuracy", "Accuracy", "F1",
"Sensitivity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence",
"Prevalence", "Support", "Confusion Matrix", "Family",
"Dependent"
)
)
expect_equal(
length(multinom_baseline_random_eval_class$`Confusion Matrix`),
30
)
expect_equal(
colnames(multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]),
c("Class", "Prediction", "Target", "Pos_0", "Pos_1", "N")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Prediction,
c("0", "1", "0", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Target,
c("0", "0", "1", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_0,
c("TP", "FN", "FP", "TN")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_1,
c("TN", "FP", "FN", "TP")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$N,
c(14, 2, 4, 5)
)
expect_equal(
multinom_baseline_random_eval_class$Repetition,
rep(1:10, each = 3)
)
expect_equal(
multinom_baseline_random_eval_class$Class,
as.character(rep(1:3, 10))
)
expect_equal(sum(multinom_baseline_random_eval_class$`Balanced Accuracy`),
17.01141,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Accuracy),
18.4,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$F1),
12.42289,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Sensitivity),
12.68254,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Pos Pred Value`),
12.63315,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Neg Pred Value`),
21.37366,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Kappa),
3.961655,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Rate`),
4.2,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Prevalence`),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Prevalence),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Support),
250,
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Family,
rep("binomial", 30),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Dependent,
rep("targets_3", 30),
tolerance = 1e-4
)
}
## Custom random generator fn
# Multinomial with custom random generator function
# that creates very "certain" predictions
# (once softmax is applied)
rcertain <- function(n) {
(runif(n, min = 1, max = 100)^1.4) / 100
}
xpectr::set_test_seed(1)
multinom_baseline_certain <- baseline(
test_data = different_targets,
dependent_col = "targets_3",
n = 10, family = "multinomial",
metrics = "all",
random_generator_fn = rcertain
)
expect_equal(
names(multinom_baseline_certain),
c(
"summarized_metrics", "summarized_class_level_results",
"random_evaluations"
)
)
multinom_baseline_summ <- multinom_baseline_certain$summarized_metrics
multinom_baseline_class <- tidyr::unnest(multinom_baseline_certain$summarized_class_level_results, "Results")
multinom_baseline_random_eval_summ <- multinom_baseline_certain$random_evaluations
multinom_baseline_random_eval_class <- dplyr::bind_rows(multinom_baseline_certain$random_evaluations$`Class Level Results`)
# Summarized results
if (TRUE) {
# # Summarized results
expect_equal(
colnames(multinom_baseline_summ),
c(
"Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate",
"Detection Prevalence", "Weighted Detection Prevalence", "Prevalence",
"Weighted Prevalence", "False Neg Rate", "Weighted False Neg Rate",
"False Pos Rate", "Weighted False Pos Rate", "False Discovery Rate",
"Weighted False Discovery Rate", "False Omission Rate", "Weighted False Omission Rate",
"Threat Score", "Weighted Threat Score"
)
)
expect_equal(
multinom_baseline_summ$Measure,
c(
"Mean", "Median", "SD", "IQR", "Max",
"Min", "NAs", "INFs", "CL_Max",
"CL_Min", "CL_NAs", "CL_INFs", "All_1", "All_2", "All_3"
)
)
expect_equal(multinom_baseline_summ$`Overall Accuracy`,
c(
0.416, 0.4, 0.138820427571417, 0.23, 0.64, 0.24, 0, 0, NA, NA, NA, NA, 0.36,
0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Balanced Accuracy`,
c(
0.562285052910053, 0.552331349206349, 0.102909289295557, 0.16025958994709,
0.73776455026455, 0.427744708994709, 0, 0,
0.845238095238095, 0.349206349206349, 0, 0, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$F1,
c(
0.410443406496038, 0.393545751633987, 0.135473485139399, 0.213743453255837,
0.638304093567251, 0.237745098039216, 0, 0,
0.75, 0.125, 0, 0, NA, NA, NA
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Sensitivity,
c(
0.416931216931217, 0.402116402116402, 0.137153997558857, 0.207671957671958,
0.656084656084656, 0.232804232804233, 0, 0,
0.857142857142857, 0.111111111111111, 0, 0, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Specificity,
c(
0.707638888888889, 0.702546296296296, 0.0688421238509424, 0.112847222222222,
0.819444444444444, 0.622685185185185, 0, 0,
0.888888888888889, 0.5, 0, 0, 0.666666666666667, 0.666666666666667,
0.666666666666667
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Pos Pred Value`,
c(
0.416391293891294, 0.395238095238095, 0.141112097555744, 0.232296176046176,
0.644444444444444, 0.24537037037037, 0, 0,
0.714285714285714, 0.111111111111111, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Neg Pred Value`,
c(
0.709738237625235, 0.703667153996101, 0.0702194379364757, 0.112253271754046,
0.824780701754386, 0.620098039215686, 0, 0,
0.9375, 0.533333333333333, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$AUC,
c(
0.562375073486185, 0.550117577895356, 0.124339882882094, 0.158877131099353,
0.789535567313345, 0.387125220458554, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Kappa,
c(
0.122510558981315, 0.102170581409235, 0.204023225460463, 0.328600213517171,
0.462128481918942, -0.1375345264999, 0, 0,
0.635036496350365, -0.277372262773723, 0, 0, 0, 0, 0
),
tolerance = 1e-3
)
expect_equal(
multinom_baseline_summ$MCC,
c(0.125850827588575, 0.106457152301142, 0.208258245757672, 0.337236969494147,
0.469598884822668, -0.137349796341982, 0, 0, NA, NA, NA, NA,
0, 0, 0),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Detection Rate`,
c(
0.138666666666667, 0.133333333333333, 0.0462734758571391, 0.0766666666666667,
0.213333333333333, 0.08, 0, 0,
0.28, 0.04, 0, 0, 0.12, 0.0933333333333333, 0.12
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 3.70074341541719e-17,
5.55111512312578e-17, 0.333333333333333, 0.333333333333333, 0,
0, 0.48, 0.16, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$Prevalence,
c(
0.333333333333333, 0.333333333333333, 0, 0, 0.333333333333333,
0.333333333333333, 0, 0, 0.36, 0.28, 0,
0, 0.333333333333333, 0.333333333333333, 0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$Accuracy,
c(
0.610666666666667, 0.6, 0.0925469517142782, 0.153333333333333,
0.76, 0.493333333333333, 0, 0,
0.84, 0.4, 0, 0, 0.573333333333333, 0.52, 0.573333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Neg Rate`,
c(
0.583068783068783, 0.597883597883598, 0.137153997558857, 0.207671957671958,
0.767195767195767, 0.343915343915344, 0, 0,
0.888888888888889, 0.142857142857143, 0, 0, 0.666666666666667,
0.666666666666667, 0.666666666666667
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Pos Rate`,
c(
0.292361111111111, 0.297453703703704, 0.0688421238509424, 0.112847222222222,
0.377314814814815, 0.180555555555556, 0, 0,
0.5, 0.111111111111111, 0, 0, 0.333333333333333, 0.333333333333333,
0.333333333333333
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Discovery Rate`,
c(
0.583608706108706, 0.604761904761905, 0.141112097555744, 0.232296176046176,
0.75462962962963, 0.355555555555556, 0, 0,
0.888888888888889, 0.285714285714286, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`False Omission Rate`,
c(
0.290261762374765, 0.296332846003899, 0.0702194379364757, 0.112253271754046,
0.379901960784314, 0.175219298245614, 0, 0,
0.466666666666667, 0.0625, 0, 0, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(
multinom_baseline_summ$`Threat Score`,
c(0.272473109678992, 0.253357753357753, 0.111848069715885, 0.169954378410261,
0.475058275058275, 0.138095238095238, 0, 0, 0.6, 0.0666666666666667,
0, 0, 0.12, 0.0933333333333333, 0.12),
tolerance = 1e-5
)
# Weighted
expect_equal(multinom_baseline_summ$`Weighted Balanced Accuracy`,
c(
0.561458333333333, 0.553819444444444, 0.103263185776414, 0.169270833333333,
0.729166666666667, 0.434027777777778, 0, 0,
NA, NA, NA, NA, 0.5, 0.5, 0.5
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted F1`,
c(
0.411579872140244, 0.395911764705882, 0.137025880250425, 0.224998773672148,
0.629368421052632, 0.246764705882353, 0, 0, NA, NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Sensitivity`,
c(
0.416, 0.4, 0.138820427571417, 0.23, 0.64, 0.24, 0, 0, NA,
NA, NA, NA, 0.36,
0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Specificity`,
c(
0.706916666666667, 0.707638888888889, 0.0680290890839442, 0.108541666666667,
0.818333333333333, 0.628055555555556, 0, 0, NA,
NA, NA, NA, 0.64, 0.72, 0.64
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Pos Pred Value`,
c(
0.419315295815296, 0.40152380952381, 0.142644759250397, 0.24119733044733,
0.642666666666667, 0.256111111111111, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Neg Pred Value`,
c(
0.70546650298446, 0.700627192982456, 0.0712309833412443, 0.120213692224528,
0.815763157894737, 0.619705882352941, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Kappa`,
c(
0.12105498715267, 0.105437069515008, 0.205614210088936, 0.345995736389319,
0.448295840764429, -0.126347507597995, 0, 0, NA,
NA, NA, NA, 0, 0, 0
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Detection Rate`,
c(
0.14016, 0.1344, 0.0476443117742763, 0.0836, 0.2112, 0.0832,
0, 0, NA, NA, NA, NA, 0.1296, 0.0784, 0.1296
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Detection Prevalence`,
c(
0.33408, 0.3312, 0.00592936196679991, 0.00639999999999996,
0.3472, 0.328, 0, 0, NA,
NA, NA, NA, 0.36, 0.28, 0.36
),
tolerance = 1e-3
)
expect_equal(multinom_baseline_summ$`Weighted Prevalence`,
c(
0.3376, 0.3376, 0, 0, 0.3376, 0.3376, 0, 0, NA,
NA, NA, NA, 0.3376, 0.3376, 0.3376
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted Accuracy`,
c(
0.60864, 0.6016, 0.0927576915049816, 0.1576, 0.7536, 0.4976,
0, 0, NA, NA, NA, NA, 0.5616, 0.5392, 0.5616
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Neg Rate`,
c(
0.584, 0.6, 0.138820427571417, 0.23, 0.76, 0.36, 0, 0, NA, NA, NA, NA, 0.64,
0.72, 0.64
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Pos Rate`,
c(
0.293083333333333, 0.292361111111111, 0.0680290890839442, 0.108541666666667,
0.371944444444444, 0.181666666666667, 0, 0, NA,
NA, NA, NA, 0.36, 0.28, 0.36
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Discovery Rate`,
c(
0.580684704184704, 0.59847619047619, 0.142644759250396, 0.24119733044733,
0.743888888888889, 0.357333333333333, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multinom_baseline_summ$`Weighted False Omission Rate`,
c(
0.29453349701554, 0.299372807017544, 0.0712309833412443, 0.120213692224528,
0.380294117647059, 0.184236842105263, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(
multinom_baseline_summ$`Weighted Threat Score`,
c(0.273521485703839, 0.255824175824176, 0.112601755397812, 0.179807138939492,
0.465062937062937, 0.143809523809524, 0, 0, NA, NA, NA, NA, 0.1296,
0.0784, 0.1296),
tolerance = 1e-5
)
}
# Summarized class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_class),
c(
"Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate",
"False Discovery Rate", "False Omission Rate", "Threat Score"
)
)
expect_equal(
multinom_baseline_class$Class,
as.character(rep(1:3, each = 10))
)
expect_equal(
multinom_baseline_class$Measure,
rep(c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0", "All_1"), 3)
)
expect_equal(sum(multinom_baseline_class$`Balanced Accuracy`), 10.68149, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Accuracy), 11.08082, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$F1, na.rm = TRUE), 7.599136, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Sensitivity), 9.372747, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Specificity), 12.17256, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Pos Pred Value`, na.rm = T), 7.221436, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Neg Pred Value`, na.rm = T), 11.1646, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Kappa), 3.323803, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Rate`), 3.121808, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Detection Prevalence`), 7.440557, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$Prevalence), 6)
expect_equal(sum(multinom_baseline_class$`False Neg Rate`), 11.01402, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Pos Rate`), 7.155891, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Discovery Rate`, na.rm = TRUE), 10.19412, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`False Omission Rate`, na.rm = TRUE), 4.99052, tolerance = 1e-4)
expect_equal(sum(multinom_baseline_class$`Threat Score`), 5.3493824364086, tolerance = 1e-4)
}
# Random evaluations results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_summ),
c(
"Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate",
"Detection Prevalence", "Weighted Detection Prevalence", "Prevalence",
"Weighted Prevalence", "False Neg Rate", "Weighted False Neg Rate",
"False Pos Rate", "Weighted False Pos Rate", "False Discovery Rate",
"Weighted False Discovery Rate", "False Omission Rate", "Weighted False Omission Rate",
"Threat Score", "Weighted Threat Score", "Predictions", "ROC",
"Confusion Matrix", "Class Level Results", "Process", "Dependent"
)
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$`Confusion Matrix`[[2]]),
c("Repetition", "Prediction", "Target", "N")
)
expect_equal(
colnames(multinom_baseline_random_eval_summ$Predictions[[1]]),
c("Repetition", "Target", "Prediction", "Predicted Class")
)
expect_equal(
multinom_baseline_random_eval_summ$Repetition,
1:10
)
expect_equal(multinom_baseline_random_eval_summ$`Overall Accuracy`,
c(0.28, 0.52, 0.32, 0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Balanced Accuracy`,
c(
0.458498677248677, 0.628637566137566, 0.498015873015873, 0.665178571428571,
0.523974867724868, 0.73776455026455, 0.466104497354497, 0.636243386243386,
0.427744708994709, 0.580687830687831
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Balanced Accuracy`,
c(
0.454861111111111, 0.631944444444444, 0.486111111111111, 0.670138888888889,
0.524305555555556, 0.729166666666667, 0.461805555555556, 0.638888888888889,
0.434027777777778, 0.583333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Accuracy,
c(
0.52, 0.68, 0.546666666666667, 0.706666666666667, 0.573333333333333,
0.76, 0.52, 0.68, 0.493333333333333, 0.626666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Accuracy`,
c(
0.5136, 0.6768, 0.536, 0.7088, 0.5744, 0.7536, 0.5168, 0.68,
0.4976, 0.6288
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$F1,
c(
0.283060592658116, 0.494896331738437, 0.324074074074074, 0.551190476190476,
0.347276688453159, 0.638304093567251, 0.276960784313726, 0.511111111111111,
0.237745098039216, 0.439814814814815
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted F1`,
c(
0.282848297213622, 0.505397129186603, 0.31, 0.561, 0.346823529411765,
0.629368421052632, 0.270882352941176, 0.517714285714286, 0.246764705882353,
0.445
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Sensitivity,
c(
0.28042328042328, 0.502645502645503, 0.338624338624339, 0.55026455026455,
0.365079365079365, 0.656084656084656, 0.291005291005291, 0.513227513227513,
0.232804232804233, 0.439153439153439
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Sensitivity`,
c(0.28, 0.52, 0.32, 0.56, 0.36, 0.64, 0.28, 0.52, 0.24, 0.44),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Specificity,
c(
0.636574074074074, 0.75462962962963, 0.657407407407407, 0.780092592592593,
0.68287037037037, 0.819444444444444, 0.641203703703704, 0.759259259259259,
0.622685185185185, 0.722222222222222
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Specificity`,
c(
0.629722222222222, 0.743888888888889, 0.652222222222222, 0.780277777777778,
0.688611111111111, 0.818333333333333, 0.643611111111111, 0.757777777777778,
0.628055555555556, 0.726666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Pos Pred Value`,
c(
0.286904761904762, 0.512121212121212, 0.317460317460317, 0.562770562770563,
0.340740740740741, 0.644444444444444, 0.272619047619048, 0.531746031746032,
0.24537037037037, 0.44973544973545
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Pos Pred Value`,
c(
0.287, 0.513090909090909, 0.307301587301587, 0.573506493506494,
0.344, 0.642666666666667, 0.270428571428571, 0.54, 0.256111111111111,
0.459047619047619
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Neg Pred Value`,
c(
0.634204793028322, 0.761904761904762, 0.662037037037037, 0.78042328042328,
0.687426900584795, 0.824780701754386, 0.645315904139434, 0.761283550757235,
0.620098039215686, 0.719907407407407
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Neg Pred Value`,
c(
0.62716339869281, 0.761904761904762, 0.65, 0.780634920634921,
0.683754385964912, 0.815763157894737, 0.638274509803922, 0.759964012595592,
0.619705882352941, 0.7175
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$AUC,
c(
0.413286302175191, 0.663139329805996, 0.494415049970605, 0.668430335097002,
0.535273368606702, 0.789535567313345, 0.491769547325103, 0.564961787184009,
0.387125220458554, 0.615814226925338
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Kappa,
c(
-0.0806325398580661, 0.257340370152257, -0.0133650986753176,
0.332217431748538, 0.0411382701447751, 0.462128481918942, -0.0736440767694057,
0.274254384977631, -0.1375345264999, 0.163202892673696
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Kappa`,
c(
-0.087718063681632, 0.261854540403706, -0.0360401459854014,
0.342286889780485, 0.0416223142124975, 0.448295840764429, -0.0823426204548178,
0.279686799267905, -0.126347507597995, 0.169251824817518
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$MCC,
c(-0.0871673257375417, 0.269468324571013, -0.0169082125603865,
0.339040525810531, 0.0486630968728153, 0.469598884822668, -0.0726394381146181,
0.281551008833778, -0.137349796341982, 0.164251207729469),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Rate`,
c(
0.0933333333333333, 0.173333333333333, 0.106666666666667, 0.186666666666667,
0.12, 0.213333333333333, 0.0933333333333333, 0.173333333333333,
0.08, 0.146666666666667
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Detection Rate`,
c(
0.0944, 0.1808, 0.1024, 0.192, 0.12, 0.2112, 0.0912, 0.1776,
0.0832, 0.1488
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Detection Prevalence`,
c(
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.333333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Detection Prevalence`,
c(
0.3376, 0.3472, 0.3312, 0.3376, 0.328, 0.3312, 0.328, 0.3376,
0.3312, 0.3312
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$Prevalence,
c(
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333,
0.3333333, 0.3333333, 0.3333333, 0.3333333, 0.3333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted Prevalence`,
c(
0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376, 0.3376,
0.3376, 0.3376
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Neg Rate`,
c(
0.71957671957672, 0.497354497354497, 0.661375661375661, 0.44973544973545,
0.634920634920635, 0.343915343915344, 0.708994708994709, 0.486772486772487,
0.767195767195767, 0.560846560846561
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Neg Rate`,
c(0.72, 0.48, 0.68, 0.44, 0.64, 0.36, 0.72, 0.48, 0.76, 0.56),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Pos Rate`,
c(
0.363425925925926, 0.24537037037037, 0.342592592592593, 0.219907407407407,
0.31712962962963, 0.180555555555556, 0.358796296296296, 0.240740740740741,
0.377314814814815, 0.277777777777778
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Pos Rate`,
c(
0.370277777777778, 0.256111111111111, 0.347777777777778, 0.219722222222222,
0.311388888888889, 0.181666666666667, 0.356388888888889, 0.242222222222222,
0.371944444444444, 0.273333333333333
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Discovery Rate`,
c(
0.713095238095238, 0.487878787878788, 0.682539682539683, 0.437229437229437,
0.659259259259259, 0.355555555555556, 0.727380952380952, 0.468253968253968,
0.75462962962963, 0.55026455026455
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Discovery Rate`,
c(
0.713, 0.486909090909091, 0.692698412698413, 0.426493506493507,
0.656, 0.357333333333333, 0.729571428571429, 0.46, 0.743888888888889,
0.540952380952381
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`False Omission Rate`,
c(
0.365795206971678, 0.238095238095238, 0.337962962962963, 0.21957671957672,
0.312573099415205, 0.175219298245614, 0.354684095860566, 0.238716449242765,
0.379901960784314, 0.280092592592593
),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_summ$`Weighted False Omission Rate`,
c(
0.37283660130719, 0.238095238095238, 0.35, 0.219365079365079,
0.316245614035088, 0.184236842105263, 0.361725490196078, 0.240035987404408,
0.380294117647059, 0.2825
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Threat Score`,
c(0.166199813258637, 0.342450142450142, 0.200396825396825, 0.385281385281385,
0.223443223443223, 0.475058275058275, 0.165079365079365, 0.345454545454545,
0.138095238095238, 0.283272283272283),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Weighted Threat Score`,
c(0.166162464985994, 0.352068376068376, 0.189761904761905, 0.394285714285714,
0.224175824175824, 0.465062937062937, 0.161142857142857, 0.351272727272727,
0.143809523809524, 0.287472527472527),
tolerance = 1e-4
)
expect_true(
as.character(multinom_baseline_random_eval_summ$Process[[1]]) %in%
paste0("---\nProcess Information\n---\nTarget column: targets_3\nPr",
"ediction columns: 1, 2, 3\nFamily / type: Multinomial\nClass",
"es: 1, 2, 3\nSoftmax: Not applied\nTarget counts: total=25, ",
"1=9, 2=7, 3=9\nPrediction counts: total=25, 1=10, 2=7, 3=8\n",
"Locale (LC_ALL): \n ",
c("en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
"C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8",
Sys.getlocale()),
"\n---"))
expect_equal(
multinom_baseline_random_eval_summ$Dependent,
rep("targets_3", 10)
)
expect_equal(
length(multinom_baseline_random_eval_summ$Predictions),
10
)
expect_equal(
length(multinom_baseline_random_eval_summ$`Confusion Matrix`),
10
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$Target,
as.character(c(1, 2, 2, 3, 1, 3, 3, 2, 2, 1, 1, 1, 2, 1, 2, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3))
)
expect_equal(
multinom_baseline_random_eval_summ$Predictions[[1]]$`Predicted Class`,
c(
"2", "2", "3", "1", "2", "3", "1", "3", "3", "2", "3", "3",
"1", "2", "1", "1", "1", "1", "2", "1", "1", "3", "1", "2", "3"
)
)
preds <- dplyr::bind_rows(multinom_baseline_random_eval_summ$Predictions[[1]]$Prediction)
expect_equal(preds$`1`,
c(
0.0292644714588426, 0.0141597631269381, 0.298438639380654,
0.80940820303688, 0.0937005066362035, 0.35235101836276, 0.975413098798697,
0.175578647077284, 0.262771970923691, 0.00356192768281427, 0.0101813747120091,
0.124070740002606, 0.914437937270295, 0.0345747630499071, 0.443603716122321,
0.754396177250108, 0.622702608502644, 0.855071475511674, 0.149160434731838,
0.732327772925981, 0.704499634048166, 0.0395430319094823, 0.771078117207197,
0.311489940634356, 0.240482208017168
),
tolerance = 1e-4
)
expect_equal(preds$`2`,
c(
0.79920110839287, 0.864873705929184, 0.149529614257696, 0.0748305440084275,
0.472042382499711, 0.00209808185776226, 0.00727516403195308,
0.0559580981939634, 0.0479065485802701, 0.953560037716267, 0.0652172908723352,
0.252080524884308, 0.0636526992714326, 0.593734385511715, 0.263207821862484,
0.116691007284114, 0.0136173006382093, 0.119886570376107, 0.735943777020592,
0.0149628131301268, 0.00263470364120449, 0.0254377163472207,
0.125594895133848, 0.373604359020064, 0.284343108476632
),
tolerance = 1e-4
)
expect_equal(preds$`3`,
c(
0.171534420148287, 0.120966530943878, 0.55203174636165, 0.115761252954693,
0.434257110864085, 0.645550899779478, 0.0173117371693497, 0.768463254728752,
0.689321480496038, 0.0428780346009194, 0.924601334415656, 0.623848735113086,
0.0219093634582727, 0.371690851438378, 0.293188462015195, 0.128912815465778,
0.363680090859147, 0.0250419541122186, 0.11489578824757, 0.252709413943892,
0.29286566231063, 0.935019251743297, 0.103326987658955, 0.31490570034558,
0.4751746835062
),
tolerance = 1e-4
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Prediction,
c("1", "2", "3", "1", "2", "3", "1", "2", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$Target,
c("1", "1", "1", "2", "2", "2", "3", "3", "3")
)
expect_equal(
multinom_baseline_random_eval_summ$`Confusion Matrix`[[1]]$N,
c(2L, 5L, 2L, 2L, 2L, 3L, 6L, 0L, 3L)
)
ROC <- multinom_baseline_random_eval_summ$ROC[[1]]
expect_equal(
names(ROC$rocs),
c("1/2", "1/3", "2/3")
)
expect_equal(
ROC$rocs$`1/2`[[1]]$sensitivities,
c(
1, 0.857142857142857, 0.857142857142857, 0.857142857142857,
0.714285714285714, 0.714285714285714, 0.571428571428571, 0.428571428571429,
0.285714285714286, 0.142857142857143, 0.142857142857143, 0.142857142857143,
0.142857142857143, 0.142857142857143, 0, 0, 0
)
)
expect_equal(
ROC$rocs$`1/2`[[2]]$sensitivities,
c(
1, 0.888888888888889, 0.888888888888889, 0.777777777777778,
0.777777777777778, 0.666666666666667, 0.555555555555556, 0.444444444444444,
0.444444444444444, 0.333333333333333, 0.333333333333333, 0.222222222222222,
0.222222222222222, 0.222222222222222, 0.222222222222222, 0.111111111111111,
0
)
)
expect_equal(
ROC$rocs$`1/2`[[1]]$specificities,
c(
0, 0, 0.111111111111111, 0.222222222222222, 0.222222222222222,
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.333333333333333,
0.333333333333333, 0.444444444444444, 0.555555555555556, 0.666666666666667,
0.777777777777778, 0.777777777777778, 0.888888888888889, 1
)
)
expect_equal(
ROC$rocs$`1/2`[[2]]$specificities,
c(
0, 0, 0.142857142857143, 0.142857142857143, 0.285714285714286,
0.285714285714286, 0.285714285714286, 0.285714285714286, 0.428571428571429,
0.428571428571429, 0.571428571428571, 0.571428571428571, 0.714285714285714,
0.857142857142857, 1, 1, 1
)
)
}
# Random evaluations class level results
if (TRUE) {
expect_equal(
colnames(multinom_baseline_random_eval_class),
c(
"Repetition", "Class", "Balanced Accuracy", "Accuracy", "F1",
"Sensitivity", "Specificity", "Pos Pred Value", "Neg Pred Value",
"Kappa", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score", "Support", "Confusion Matrix", "Family", "Dependent"
)
)
expect_equal(
length(multinom_baseline_random_eval_class$`Confusion Matrix`),
30
)
expect_equal(
colnames(multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]),
c("Class", "Prediction", "Target", "Pos_0", "Pos_1", "N")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Prediction,
c("0", "1", "0", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Target,
c("0", "0", "1", "1")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_0,
c("TP", "FN", "FP", "TN")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$Pos_1,
c("TN", "FP", "FN", "TP")
)
expect_equal(
multinom_baseline_random_eval_class$`Confusion Matrix`[[1]]$N,
c(8, 8, 7, 2)
)
expect_equal(
multinom_baseline_random_eval_class$Repetition,
rep(1:10, each = 3)
)
expect_equal(
multinom_baseline_random_eval_class$Class,
as.character(rep(1:3, 10))
)
expect_equal(sum(multinom_baseline_random_eval_class$`Balanced Accuracy`),
16.86855,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Accuracy),
18.32,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$F1),
12.3133,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Sensitivity),
12.50794,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Specificity),
21.22917,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Pos Pred Value`),
12.49174,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Neg Pred Value`),
21.29215,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Kappa),
3.675317,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Rate`),
4.16,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`Detection Prevalence`),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Prevalence),
10,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Neg Rate`),
17.49206,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Pos Rate`),
8.770833,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Discovery Rate`),
17.50826,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$`False Omission Rate`),
8.707853,
tolerance = 1e-4
)
expect_equal(
sum(multinom_baseline_random_eval_class$`Threat Score`),
8.17419329036976,
tolerance = 1e-4
)
expect_equal(sum(multinom_baseline_random_eval_class$Support),
250,
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Family,
rep("binomial", 30),
tolerance = 1e-4
)
expect_equal(multinom_baseline_random_eval_class$Dependent,
rep("targets_3", 30),
tolerance = 1e-4
)
}
})
test_that("baseline() throws expected errors", {
# Binomial
xpectr::set_test_seed(1)
# cutoff
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = 1.1
),
"1 assertions failed:\n * Variable 'cutoff': Element 1 is not <= 1.",
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = NA
),
"1 assertions failed:\n * Variable 'cutoff': May not be NA.",
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = NULL
),
paste0(
"1 assertions failed:\n * Variable 'cutoff': Must be of type",
" 'number', not 'NULL'."
),
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
cutoff = c(0, 1)
),
"1 assertions failed:\n * Variable 'cutoff': Must have length 1.",
fixed = T
)
# positive
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = NULL
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::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 = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = NA
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is 'NA'\n * checkmate::check_string(positive): May ",
"not be NA")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = 3
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is '3'\n * checkmate::check_string(positive): Must ",
"be of type 'string', not 'double'")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = -1
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is '-1'\n * checkmate::check_string(positive): Must",
" be of type 'string', not 'double'")),
fixed = T
)
expect_error(xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = c("0", "1")
)),
xpectr::strip(paste0("Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is not atomic scalar\n * checkmate::check_string(po",
"sitive): Must have length 1")),
fixed = T
)
expect_error(
xpectr::strip_msg(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
family = "binomial",
positive = c(0, 1)
)),
xpectr::strip(paste0(
"Assertion failed. One of the following must apply:\n * chec",
"kmate::check_choice(positive): Must be element of set {'1','",
"2'}, but is not atomic scalar\n * checkmate::check_string(po",
"sitive): Must be of type 'string', not 'double'")),
fixed = T
)
expect_message(baseline(
train_data = participant.scores,
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"'train_data' was not used for binomial version of baseline().",
fixed = T
)
expect_message(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
family = "binomial",
random_generator_fn = rnorm
),
paste0(
"'random_generator_fn' was not default function. ",
"Note that the 'random_generator_fn' is not used in ",
"the binomial version of baseline()."
),
fixed = T
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 2,
metrics = list(
"AIC" = TRUE,
"AICc" = TRUE,
"BIC" = TRUE
),
family = "binomial"
),
"binomial baseline() does not accept the following metrics: AIC, AICc, BIC.",
fixed = T
)
expect_message(
suppressWarnings(baseline(
test_data = participant.scores,
train_data = participant.scores,
dependent_col = "score",
random_generator_fn = rnorm,
n = 10,
family = "gaussian",
parallel = FALSE
)),
paste0(
"'random_generator_fn' was not default function. ",
"Note that the 'random_generator_fn' is not used in ",
"the Gaussian version of baseline()."
)
)
expect_error(baseline(
test_data = participant.scores,
dependent_col = "score",
n = 10,
family = "gaussian"
),
paste0(
"1 assertions failed:\n * Variable 'train_data': Must be of ",
"type 'data.frame', not 'NULL'."
),
fixed = T
)
expect_error(
xpectr::strip_msg(
baseline(
train_data = participant.scores,
test_data = participant.scores,
dependent_col = "xxx",
n = 10,
family = "gaussian"
),
lowercase = TRUE
),
xpectr::strip(
paste0(
"2 assertions failed:\n * Variable 'colnames(test_data)': ",
ifelse(is_checkmate_v2_1(), "Names", ""),
" Must include the elements {xxx} ",
ifelse(is_checkmate_v2_1(), "but is missing elements {xxx}", ""),
"\n * Variable 'colnames(train_",
"data)': ",
ifelse(is_checkmate_v2_1(), "Names", ""),
" Must include the elements {xxx} ",
ifelse(is_checkmate_v2_1(), "but is missing elements {xxx}", "")
),
lowercase = TRUE
),
fixed = T
)
expect_error(
xpectr::strip_msg(
baseline(
train_data = participant.scores,
test_data = dplyr::select(participant.scores,-"score"),
dependent_col = "score",
n = 10,
family = "gaussian"
),
lowercase = TRUE
),
xpectr::strip(paste0("must include the elements {score}."), lowercase = TRUE),
fixed = T
)
expect_equal(
subtract_inf_count_from_na_count(tibble::tibble(
"Measure" = c("Mean", "NAs", "INFs", "Lol"),
"Accuracy" = c(1, 3, 4, 2),
"Balanced Accuracy" = c(3, 2, 5, 2)
)),
tibble::tibble(
"Measure" = c("Mean", "NAs", "INFs", "Lol"),
"Accuracy" = c(1, -1, 4, 2),
"Balanced Accuracy" = c(3, -3, 5, 2)
)
)
expect_error(xpectr::strip_msg(create_multinomial_baseline_evaluations(data.frame(),
dependent_col = "",
na.rm = NULL,
random_generator_fn = runif,
parallel_ = FALSE
)),
xpectr::strip(paste0("1 assertions failed:\n * Variable 'na.rm': Must be of type ",
"'logical flag', not 'NULL'.")),
fixed = TRUE
)
expect_error(xpectr::strip_msg(create_gaussian_baseline_evaluations(as.data.frame(matrix(1, 11, 1)),
data.frame(),
dependent_col = "",
na.rm = NULL
)),
xpectr::strip(paste0("1 assertions failed:\n * Variable 'na.rm': Must be of type ",
"'logical flag', not 'NULL'.")),
fixed = TRUE
)
xpectr::set_test_seed(1)
dat <- participant.scores
dat$diagnosis <- c(head(dat$diagnosis, 20), rep(2, 10))
expect_error(baseline(
test_data = dat,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"The dependent column must maximally contain 2 levels. Did you specify the correct family?",
fixed = T
)
dat$diagnosis <- as.character(dat$diagnosis)
expect_error(baseline(
test_data = dat,
dependent_col = "diagnosis",
n = 2,
family = "binomial"
),
"The dependent column must maximally contain 2 levels.",
fixed = T
)
})
test_that("multinomial baseline() works when test_data contains columns with names of the classes", {
# We simply test that we get different random evaluations
# when test_data contains columns with same names as the classes
# that could be interpreted as the probability columns
df <- tibble::tibble(
"Target" = rep(c("A", "B", "C"), 10),
"A" = rep(1, 30),
"B" = rep(0, 30),
"C" = rep(0, 30)
)
xpectr::set_test_seed(1)
multiclass_baseline <- df %>%
baseline(
dependent_col = "Target",
n = 3,
family = "multinomial",
parallel = FALSE
)
expect_equal(multiclass_baseline$random_evaluations$F1,
c(0.317714964773788, 0.338624338624339, 0.397326649958229),
tolerance = 1e-5
)
})
test_that("multinomial baseline() summarizes correctly with imbalanced dataset", {
testthat::skip_on_cran()
# We simply test that we get different random evaluations
# when test_data contains columns with same names as the classes
# that could be interpreted as the probability columns
df <- tibble::tibble("Target" = c(
rep("A", 1),
rep("A2", 1),
# rep("A3", 1),
rep("B", 50),
rep("C", 100)
))
xpectr::set_test_seed(3)
multiclass_baseline <- df %>%
baseline(
dependent_col = "Target",
n = 5,
family = "multinomial",
parallel = FALSE
)
means <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Mean",
] %>% base_deselect("Measure")
medians <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Median",
] %>% base_deselect("Measure")
mins <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Min",
] %>% base_deselect("Measure")
maxes <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "Max",
] %>% base_deselect("Measure")
sds <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "SD",
] %>% base_deselect("Measure")
IQRs <- multiclass_baseline$summarized_metrics[
multiclass_baseline$summarized_metrics[["Measure"]] == "IQR",
] %>% base_deselect("Measure")
apply_descriptor <- function(random_evals, fn) {
random_evals %>%
dplyr::summarise_if(is.numeric, .f = list(
~ fn(., na.rm = TRUE)
)) %>%
dplyr::select(-"Repetition")
}
rand_eval_means <- apply_descriptor(multiclass_baseline$random_evaluations, mean)
expect_equal(means, rand_eval_means, tolerance = 1e-5)
rand_eval_medians <- apply_descriptor(multiclass_baseline$random_evaluations, median)
expect_equal(medians, rand_eval_medians, tolerance = 1e-5)
rand_eval_sds <- apply_descriptor(multiclass_baseline$random_evaluations, sd)
expect_equal(sds, rand_eval_sds, tolerance = 1e-5)
rand_eval_IQRs <- apply_descriptor(multiclass_baseline$random_evaluations, IQR)
expect_equal(IQRs, rand_eval_IQRs, tolerance = 1e-5)
rand_eval_mins <- apply_descriptor(multiclass_baseline$random_evaluations, min)
expect_equal(mins, rand_eval_mins, tolerance = 1e-5)
rand_eval_maxes <- apply_descriptor(multiclass_baseline$random_evaluations, max)
expect_equal(maxes, rand_eval_maxes, tolerance = 1e-5)
expect_equal(multiclass_baseline$summarized_metrics$F1,
c(
0.225746415940381, 0.225746415940381, NA, 0, 0.225746415940381,
0.225746415940381, 4, 0, 0.428571428571428, 0.0512820512820513,
7, 0, NaN, NaN, NaN, NaN
),
tolerance = 1e-5
)
expect_equal(multiclass_baseline$random_evaluations$F1,
c(NaN, NaN, 0.225746415940381, NaN, NaN),
tolerance = 1e-5
)
expect_equal(legacy_unnest(multiclass_baseline$summarized_class_level_results)$F1,
c(
0.0512820512820513, 0.0512820512820513, NA, 0, 0.0512820512820513,
0.0512820512820513, 4, 0, NaN, 0.0130718954248366, 0.0622605363984674,
0.0622605363984674, 0.00948227484349775, 0.00670498084291188,
0.0689655172413793, 0.0555555555555556, 3, 0, NaN, 0.0130718954248366,
0.354358405547151, 0.354166666666667, 0.0402576466739213, 0.0614074406095921,
0.395348837209302, 0.301075268817204, 0, 0, NaN, 0.495049504950495,
0.378838880566167, 0.37410071942446, 0.0366196447359243, 0.0334224598930482,
0.428571428571428, 0.330827067669173, 0, 0, NaN, 0.793650793650794
),
tolerance = 1e-5
)
})
test_that("baseline wrappers minimal testing", {
testthat::skip_on_cran()
# These tests just show that calling with normal arg values doesn't fail
# That is, that the args are passed correctly to baseline()
# Gaussian ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(gauss_bsl <- baseline_gaussian(
test_data = participant.scores,
train_data = participant.scores,
dependent_col = "score",
n = 10,
metrics = "all",
random_effects = "(1|session)",
min_training_rows = 3,
min_training_rows_left_out = 3,
REML = FALSE,
parallel = FALSE
))
## Testing 'gauss_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(gauss_bsl$summarized_metrics),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
gauss_bsl$summarized_metrics[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_rows"),
fixed = TRUE)
expect_equal(
gauss_bsl$summarized_metrics[["RMSE"]],
c(14.98342, 13.41915, 4.93453, 0.79024, 28.97336, 12.83495, 0, 0,
12.84146),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MAE"]],
c(12.95187, 11.46669, 4.34472, 0.66819, 25.27778, 11.3, 0, 0, 11.3),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(RNG)"]],
c(0.21103, 0.189, 0.0695, 0.01113, 0.40808, 0.18077, 0, 0, 0.18087),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(IQR)"]],
c(0.53512, 0.47926, 0.17623, 0.02822, 1.03476, 0.45839, 0, 0, 0.45862),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(STD)"]],
c(0.77659, 0.69551, 0.25576, 0.04096, 1.50169, 0.66523, 0, 0, 0.66557),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["NRMSE(AVG)"]],
c(0.3865, 0.34615, 0.12729, 0.02038, 0.74738, 0.33108, 0, 0, 0.33125),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RSE"]],
c(0.68479, 0.50044, 0.57997, 0.05939, 2.33282, 0.4578, 0, 0, 0.45826),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RRSE"]],
c(0.78987, 0.7074, 0.26013, 0.04166, 1.52736, 0.67661, 0, 0, 0.67695),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RAE"]],
c(0.83286, 0.73736, 0.27938, 0.04297, 1.62546, 0.72664, 0, 0, 0.72664),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["RMSLE"]],
c(0.41389, 0.3698, 0.12935, 0.04991, 0.77487, 0.34874, 0, 0, 0.35324),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MALE"]],
c(0.34758, 0.31415, 0.09475, 0.02261, 0.61514, 0.30837, 0, 0, 0.30837),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MAPE"]],
c(0.45503, 0.36922, 0.24845, 0.07511, 1.1531, 0.32215, 0, 0, 0.36212),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["MSE"]],
c(246.41758, 180.08082, 208.70066, 21.3701, 839.45556, 164.73591,
0, 0, 164.903),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["TAE"]],
c(388.55611, 344.00073, 130.34157, 20.0457, 758.33333, 339, 0, 0,
339),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["TSE"]],
c(7392.52753, 5402.42463, 6261.01989, 641.10285, 25183.66667, 4942.07715,
0, 0, 4947.09013),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["r2m"]],
c(0, 0, 0, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["r2c"]],
c(0.42594, 0.46804, 0.17991, 0.07183, 0.68023, 0, 0, 0, 0.49599),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["AIC"]],
c(147.05357, 146.29569, 55.56405, 44.79092, 215.0563, 52.91061,
0, 0, 254.30188),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["AICc"]],
c(150.73341, 148.14184, 51.59965, 44.11329, 216.19916, 64.91061,
0, 0, 255.22496),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["BIC"]],
c(149.25056, 148.79533, 57.10576, 45.77481, 218.71293, 52.28589,
0, 0, 258.50547),
tolerance = 1e-4)
expect_equal(
gauss_bsl$summarized_metrics[["Training Rows"]],
c(17, 17, 6.68331, 6.25, 25, 6, 0, 0, 30),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(gauss_bsl$summarized_metrics),
c("Measure", "RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)",
"NRMSE(AVG)", "RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE",
"MSE", "TAE", "TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Training Rows"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(gauss_bsl$summarized_metrics),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(gauss_bsl$summarized_metrics),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(gauss_bsl$summarized_metrics),
c(9L, 22L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(gauss_bsl$summarized_metrics)),
character(0),
fixed = TRUE)
## Finished testing 'gauss_bsl$summarized_metrics' ####
## Testing 'gauss_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(gauss_bsl$random_evaluations),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
gauss_bsl$random_evaluations[["RMSE"]],
c(13.64398, 13.07772, 12.83495, 13.22401, 13.99139, 28.97336, 13.3344,
13.02982, 14.22069, 13.50391),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MAE"]],
c(11.50297, 11.3, 11.3, 11.42285, 12.21149, 25.27778, 11.34728,
11.44461, 12.22296, 11.48878),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(RNG)"]],
c(0.19217, 0.18419, 0.18077, 0.18625, 0.19706, 0.40808, 0.18781,
0.18352, 0.20029, 0.1902),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(IQR)"]],
c(0.48728, 0.46706, 0.45839, 0.47229, 0.49969, 1.03476, 0.47623,
0.46535, 0.50788, 0.48228),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(STD)"]],
c(0.70717, 0.67782, 0.66523, 0.6854, 0.72517, 1.50169, 0.69112,
0.67533, 0.73706, 0.69991),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["NRMSE(AVG)"]],
c(0.35195, 0.33734, 0.33108, 0.34112, 0.36091, 0.74738, 0.34397,
0.33611, 0.36683, 0.34834),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RSE"]],
c(0.51733, 0.47528, 0.4578, 0.48597, 0.54401, 2.33282, 0.49412,
0.4718, 0.56199, 0.50676),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RRSE"]],
c(0.71926, 0.6894, 0.67661, 0.69712, 0.73757, 1.52736, 0.70294,
0.68688, 0.74966, 0.71187),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RAE"]],
c(0.73969, 0.72664, 0.72664, 0.73454, 0.78525, 1.62546, 0.72968,
0.73593, 0.78599, 0.73878),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["RMSLE"]],
c(0.36902, 0.35433, 0.34874, 0.35807, 0.41539, 0.77487, 0.35172,
0.37058, 0.42158, 0.37456),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MALE"]],
c(0.31426, 0.30837, 0.30837, 0.31131, 0.33837, 0.61514, 0.3097,
0.31404, 0.34047, 0.31572),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MAPE"]],
c(0.35215, 0.35506, 0.35473, 0.3641, 0.4451, 1.1531, 0.32215, 0.38439,
0.44521, 0.37433),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["MSE"]],
c(186.15812, 171.02681, 164.73591, 174.87451, 195.75907, 839.45556,
177.80617, 169.77619, 202.22804, 182.35547),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["TAE"]],
c(345.08913, 339, 339, 342.68544, 366.34467, 758.33333, 340.4183,
343.33815, 366.6888, 344.66331),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["TSE"]],
c(5584.74358, 5130.8042, 4942.07715, 5246.23543, 5872.77195, 25183.66667,
5334.18525, 5093.28575, 6066.84126, 5470.66402),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["r2m"]],
c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["r2c"]],
c(0.52642, 0.40821, 0.49646, 0.45673, 0.4667, 0, 0.68023, 0.4793,
0.27596, 0.46938),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["AIC"]],
c(147.59485, 144.18876, 215.0563, 204.69939, 56.26379, 52.91061,
144.99653, 195.24105, 142.98879, 166.59566),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["AICc"]],
c(149.441, 146.18876, 216.19916, 205.89939, 68.26379, 64.91061,
146.84269, 196.50421, 144.98879, 168.09566),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["BIC"]],
c(150.09449, 146.50653, 218.71293, 208.23355, 55.63906, 52.28589,
147.49617, 198.64753, 145.30656, 169.58286),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["Training Rows"]],
c(17, 16, 25, 24, 6, 6, 17, 23, 16, 20),
tolerance = 1e-4)
expect_equal(
gauss_bsl$random_evaluations[["Dependent"]],
c("score", "score", "score", "score", "score", "score", "score",
"score", "score", "score"),
fixed = TRUE)
expect_equal(
gauss_bsl$random_evaluations[["Fixed"]],
c("1", "1", "1", "1", "1", "1", "1", "1", "1", "1"),
fixed = TRUE)
expect_equal(
gauss_bsl$random_evaluations[["Random"]],
c("(1|session)", "(1|session)", "(1|session)", "(1|session)", "(1|session)",
"(1|session)", "(1|session)", "(1|session)", "(1|session)",
"(1|session)"),
fixed = TRUE)
# Testing column names
expect_equal(
names(gauss_bsl$random_evaluations),
c("RMSE", "MAE", "NRMSE(RNG)", "NRMSE(IQR)", "NRMSE(STD)", "NRMSE(AVG)",
"RSE", "RRSE", "RAE", "RMSLE", "MALE", "MAPE", "MSE", "TAE",
"TSE", "r2m", "r2c", "AIC", "AICc", "BIC", "Predictions", "Coefficients",
"Process", "Training Rows", "Dependent", "Fixed", "Random"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(gauss_bsl$random_evaluations),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"),
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list", "integer",
"character", "character", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(gauss_bsl$random_evaluations),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "list", "list", "list", "integer", "character",
"character", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(gauss_bsl$random_evaluations),
c(10L, 27L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(gauss_bsl$random_evaluations)),
character(0),
fixed = TRUE)
## Finished testing 'gauss_bsl$random_evaluations' ####
# Binomial ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(binom_bsl <- baseline_binomial(
test_data = participant.scores,
dependent_col = "diagnosis",
n = 10,
metrics = "all",
positive = "1",
cutoff = 0.6,
parallel = FALSE
))
## Testing 'binom_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(binom_bsl$summarized_metrics),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
binom_bsl$summarized_metrics[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "All_0",
"All_1"),
fixed = TRUE)
expect_equal(
binom_bsl$summarized_metrics[["Balanced Accuracy"]],
c(0.48056, 0.47222, 0.08958, 0.10069, 0.61111, 0.31944, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Accuracy"]],
c(0.46, 0.46667, 0.09402, 0.125, 0.56667, 0.3, 0, 0, 0.4, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["F1"]],
c(0.44393, 0.52397, 0.14255, 0.22158, 0.60606, 0.23077, 0, 0, NaN,
0.75),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Sensitivity"]],
c(0.37778, 0.41667, 0.15449, 0.27778, 0.55556, 0.16667, 0, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Specificity"]],
c(0.58333, 0.58333, 0.14699, 0.22917, 0.83333, 0.41667, 0, 0, 1,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Pos Pred Value"]],
c(0.56712, 0.56696, 0.12928, 0.14062, 0.77778, 0.36364, 0, 0, NaN,
0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Neg Pred Value"]],
c(0.38664, 0.37747, 0.06811, 0.08333, 0.47619, 0.26316, 0, 0, 0.4,
NaN),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["AUC"]],
c(0.48426, 0.50231, 0.1345, 0.19329, 0.62963, 0.21759, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Lower CI"]],
c(0.27339, 0.28488, 0.12758, 0.19226, 0.42573, 0.04243, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Upper CI"]],
c(0.69513, 0.71975, 0.14244, 0.19431, 0.84678, 0.39276, 0, 0, 0.5,
0.5),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Kappa"]],
c(-0.0345, -0.05259, 0.16265, 0.18388, 0.19753, -0.32911, 0, 0,
0, 0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["MCC"]],
c(-0.04237, -0.057, 0.1866, 0.20975, 0.23757, -0.36711, 0, 0, 0,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Detection Rate"]],
c(0.22667, 0.25, 0.0927, 0.16667, 0.33333, 0.1, 0, 0, 0, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Detection Prevalence"]],
c(0.39333, 0.38333, 0.12353, 0.25, 0.53333, 0.23333, 0, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Prevalence"]],
c(0.6, 0.6, 0, 0, 0.6, 0.6, 0, 0, 0.6, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Neg Rate"]],
c(0.62222, 0.58333, 0.15449, 0.27778, 0.83333, 0.44444, 0, 0, 1,
0),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Pos Rate"]],
c(0.41667, 0.41667, 0.14699, 0.22917, 0.58333, 0.16667, 0, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Discovery Rate"]],
c(0.43288, 0.43304, 0.12928, 0.14062, 0.63636, 0.22222, 0, 0, NaN,
0.4),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["False Omission Rate"]],
c(0.61336, 0.62253, 0.06811, 0.08333, 0.73684, 0.52381, 0, 0, 0.6,
NaN),
tolerance = 1e-4)
expect_equal(
binom_bsl$summarized_metrics[["Threat Score"]],
c(0.29478, 0.355, 0.11526, 0.17874, 0.43478, 0.13043, 0, 0, 0, 0.6),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(binom_bsl$summarized_metrics),
c("Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI",
"Upper CI", "Kappa", "MCC", "Detection Rate", "Detection Prevalence",
"Prevalence", "False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(binom_bsl$summarized_metrics),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"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(binom_bsl$summarized_metrics),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(binom_bsl$summarized_metrics),
c(10L, 21L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(binom_bsl$summarized_metrics)),
character(0),
fixed = TRUE)
## Finished testing 'binom_bsl$summarized_metrics' ####
## Testing 'binom_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(binom_bsl$random_evaluations),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
binom_bsl$random_evaluations[["Balanced Accuracy"]],
c(0.52778, 0.56944, 0.61111, 0.45833, 0.45833, 0.31944, 0.55556,
0.375, 0.44444, 0.48611),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Accuracy"]],
c(0.53333, 0.56667, 0.56667, 0.46667, 0.46667, 0.3, 0.53333, 0.33333,
0.4, 0.43333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["F1"]],
c(0.58824, 0.60606, 0.51852, 0.52941, 0.52941, 0.27586, 0.53333,
0.23077, 0.30769, 0.32),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Sensitivity"]],
c(0.55556, 0.55556, 0.38889, 0.5, 0.5, 0.22222, 0.44444, 0.16667,
0.22222, 0.22222),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Specificity"]],
c(0.5, 0.58333, 0.83333, 0.41667, 0.41667, 0.41667, 0.66667, 0.58333,
0.66667, 0.75),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Pos Pred Value"]],
c(0.625, 0.66667, 0.77778, 0.5625, 0.5625, 0.36364, 0.66667, 0.375,
0.5, 0.57143),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Neg Pred Value"]],
c(0.42857, 0.46667, 0.47619, 0.35714, 0.35714, 0.26316, 0.44444,
0.31818, 0.36364, 0.3913),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["AUC"]],
c(0.60185, 0.62963, 0.62963, 0.45833, 0.48611, 0.21759, 0.51852,
0.35648, 0.37963, 0.56481),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Lower CI"]],
c(0.39309, 0.42573, 0.41248, 0.24254, 0.27075, 0.04243, 0.29902,
0.13493, 0.17016, 0.34279),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Upper CI"]],
c(0.81062, 0.83353, 0.84678, 0.67413, 0.70148, 0.39276, 0.73802,
0.57803, 0.5891, 0.78684),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Kappa"]],
c(0.05405, 0.13333, 0.19753, -0.08108, -0.08108, -0.32911, 0.10256,
-0.21951, -0.09756, -0.0241),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["MCC"]],
c(0.05455, 0.13608, 0.23757, -0.08183, -0.08183, -0.36711, 0.11111,
-0.27696, -0.12309, -0.03217),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Detection Rate"]],
c(0.33333, 0.33333, 0.23333, 0.3, 0.3, 0.13333, 0.26667, 0.1, 0.13333,
0.13333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Detection Prevalence"]],
c(0.53333, 0.5, 0.3, 0.53333, 0.53333, 0.36667, 0.4, 0.26667, 0.26667,
0.23333),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Prevalence"]],
c(0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Neg Rate"]],
c(0.44444, 0.44444, 0.61111, 0.5, 0.5, 0.77778, 0.55556, 0.83333,
0.77778, 0.77778),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Pos Rate"]],
c(0.5, 0.41667, 0.16667, 0.58333, 0.58333, 0.58333, 0.33333, 0.41667,
0.33333, 0.25),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Discovery Rate"]],
c(0.375, 0.33333, 0.22222, 0.4375, 0.4375, 0.63636, 0.33333, 0.625,
0.5, 0.42857),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["False Omission Rate"]],
c(0.57143, 0.53333, 0.52381, 0.64286, 0.64286, 0.73684, 0.55556,
0.68182, 0.63636, 0.6087),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Threat Score"]],
c(0.41667, 0.43478, 0.35, 0.36, 0.36, 0.16, 0.36364, 0.13043, 0.18182,
0.19048),
tolerance = 1e-4)
expect_equal(
binom_bsl$random_evaluations[["Dependent"]],
c("diagnosis", "diagnosis", "diagnosis", "diagnosis", "diagnosis",
"diagnosis", "diagnosis", "diagnosis", "diagnosis", "diagnosis"),
fixed = TRUE)
# Testing column names
expect_equal(
names(binom_bsl$random_evaluations),
c("Balanced Accuracy", "Accuracy", "F1", "Sensitivity", "Specificity",
"Pos Pred Value", "Neg Pred Value", "AUC", "Lower CI", "Upper CI",
"Kappa", "MCC", "Detection Rate", "Detection Prevalence", "Prevalence",
"False Neg Rate", "False Pos Rate", "False Discovery Rate",
"False Omission Rate", "Threat Score", "Predictions", "ROC",
"Confusion Matrix", "Process", "Dependent"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(binom_bsl$random_evaluations),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list",
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list", "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::element_types(binom_bsl$random_evaluations),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "list", "list", "list", "list", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(binom_bsl$random_evaluations),
c(10L, 25L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(binom_bsl$random_evaluations)),
character(0),
fixed = TRUE)
## Finished testing 'binom_bsl$random_evaluations' ####
# Multinomial ####
xpectr::set_test_seed(42)
xpectr::suppress_mw(multinom_bsl <- baseline_multinomial(
test_data = predicted.musicians %>% dplyr::filter(Classifier == "e1071_svm", `Fold Column` == ".folds_1"),
dependent_col = "Target",
n = 10,
metrics = "all",
random_generator_fn = rnorm,
parallel = FALSE
))
## Testing 'multinom_bsl$summarized_metrics' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19078 <- multinom_bsl$summarized_metrics
# Testing class
expect_equal(
class(output_19078),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19078[["Measure"]],
c("Mean", "Median", "SD", "IQR", "Max", "Min", "NAs", "INFs", "CL_Max",
"CL_Min", "CL_NAs", "CL_INFs", "All_A", "All_B", "All_C", "All_D"),
fixed = TRUE)
expect_equal(
output_19078[["Overall Accuracy"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Balanced Accuracy"]],
c(0.51667, 0.51111, 0.04099, 0.04722, 0.58889, 0.45556, 0, 0, 0.64444,
0.38889, 0, 0, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Balanced Accuracy"]],
c(0.51667, 0.51111, 0.04099, 0.04722, 0.58889, 0.45556, 0, 0, NA,
NA, NA, NA, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Accuracy"]],
c(0.6375, 0.63333, 0.03074, 0.03542, 0.69167, 0.59167, 0, 0, 0.76667,
0.55, 0, 0, 0.625, 0.625, 0.625, 0.625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Accuracy"]],
c(0.6375, 0.63333, 0.03074, 0.03542, 0.69167, 0.59167, 0, 0, NA,
NA, NA, NA, 0.625, 0.625, 0.625, 0.625),
tolerance = 1e-4)
expect_equal(
output_19078[["F1"]],
c(0.2719, 0.26527, 0.06209, 0.0739, 0.37897, 0.17845, 0, 0, 0.46154,
0.06897, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted F1"]],
c(0.2719, 0.26527, 0.06209, 0.0739, 0.37897, 0.17845, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Sensitivity"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, 0.53333,
0.06667, 0, 0, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Sensitivity"]],
c(0.275, 0.26667, 0.06149, 0.07083, 0.38333, 0.18333, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Specificity"]],
c(0.75833, 0.75556, 0.0205, 0.02361, 0.79444, 0.72778, 0, 0, 0.88889,
0.66667, 0, 0, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Specificity"]],
c(0.75833, 0.75556, 0.0205, 0.02361, 0.79444, 0.72778, 0, 0, NA,
NA, NA, NA, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Pos Pred Value"]],
c(0.27442, 0.26546, 0.06486, 0.08217, 0.3837, 0.17897, 0, 0, 0.54545,
0.07143, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Pos Pred Value"]],
c(0.27442, 0.26546, 0.06486, 0.08217, 0.3837, 0.17897, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Neg Pred Value"]],
c(0.75864, 0.75571, 0.02063, 0.02323, 0.79555, 0.72796, 0, 0, 0.82051,
0.69565, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Neg Pred Value"]],
c(0.75864, 0.75571, 0.02063, 0.02323, 0.79555, 0.72796, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["AUC"]],
c(0.50715, 0.50241, 0.03493, 0.04861, 0.55222, 0.44815, 0, 0, NA,
NA, NA, NA, 0.5, 0.5, 0.5, 0.5),
tolerance = 1e-4)
expect_equal(
output_19078[["Kappa"]],
c(0.03264, 0.02171, 0.08248, 0.09823, 0.17601, -0.09015, 0, 0, 0.31707,
-0.22727, 0, 0, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Kappa"]],
c(0.03264, 0.02171, 0.08248, 0.09823, 0.17601, -0.09015, 0, 0, NA,
NA, NA, NA, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["MCC"]],
c(0.03362, 0.02226, 0.08268, 0.09547, 0.17938, -0.08962, 0, 0, NA,
NA, NA, NA, 0, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
output_19078[["Detection Rate"]],
c(0.06875, 0.06667, 0.01537, 0.01771, 0.09583, 0.04583, 0, 0, 0.13333,
0.01667, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Detection Rate"]],
c(0.06875, 0.06667, 0.01537, 0.01771, 0.09583, 0.04583, 0, 0, NA,
NA, NA, NA, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Detection Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.35, 0.16667, 0, 0, 0.25,
0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Detection Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, NA, NA, NA, NA, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Prevalence"]],
c(0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0, NA, NA, NA, NA, 0.25, 0.25,
0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["False Neg Rate"]],
c(0.725, 0.73333, 0.06149, 0.07083, 0.81667, 0.61667, 0, 0, 0.93333,
0.46667, 0, 0, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Neg Rate"]],
c(0.725, 0.73333, 0.06149, 0.07083, 0.81667, 0.61667, 0, 0, NA,
NA, NA, NA, 0.75, 0.75, 0.75, 0.75),
tolerance = 1e-4)
expect_equal(
output_19078[["False Pos Rate"]],
c(0.24167, 0.24444, 0.0205, 0.02361, 0.27222, 0.20556, 0, 0, 0.33333,
0.11111, 0, 0, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Pos Rate"]],
c(0.24167, 0.24444, 0.0205, 0.02361, 0.27222, 0.20556, 0, 0, NA,
NA, NA, NA, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19078[["False Discovery Rate"]],
c(0.72558, 0.73454, 0.06486, 0.08217, 0.82103, 0.6163, 0, 0, 0.92857,
0.45455, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Discovery Rate"]],
c(0.72558, 0.73454, 0.06486, 0.08217, 0.82103, 0.6163, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["False Omission Rate"]],
c(0.24136, 0.24429, 0.02063, 0.02323, 0.27204, 0.20445, 0, 0, 0.30435,
0.17949, 0, 0, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted False Omission Rate"]],
c(0.24136, 0.24429, 0.02063, 0.02323, 0.27204, 0.20445, 0, 0, NA,
NA, NA, NA, NaN, NaN, NaN, NaN),
tolerance = 1e-4)
expect_equal(
output_19078[["Threat Score"]],
c(0.16053, 0.15405, 0.04233, 0.04668, 0.23562, 0.09963, 0, 0, 0.3,
0.03571, 0, 0, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
expect_equal(
output_19078[["Weighted Threat Score"]],
c(0.16053, 0.15405, 0.04233, 0.04668, 0.23562, 0.09963, 0, 0, NA,
NA, NA, NA, 0.0625, 0.0625, 0.0625, 0.0625),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(output_19078),
c("Measure", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate", "Detection Prevalence",
"Weighted Detection Prevalence", "Prevalence", "Weighted Prevalence",
"False Neg Rate", "Weighted False Neg Rate", "False Pos Rate",
"Weighted False Pos Rate", "False Discovery Rate", "Weighted False Discovery Rate",
"False Omission Rate", "Weighted False Omission Rate", "Threat Score",
"Weighted Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::smpl(xpectr::element_classes(output_19078), n = 30),
c("character", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::smpl(xpectr::element_types(output_19078), n = 30),
c("character", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19078),
c(16L, 36L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19078)),
character(0),
fixed = TRUE)
## Finished testing 'multinom_bsl$summarized_metrics' ####
summ_class_level <- legacy_unnest(multinom_bsl$summarized_class_level_results)
## Testing 'summ_class_level' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Testing class
expect_equal(
class(summ_class_level),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
xpectr::smpl(summ_class_level[["Class"]], n = 30),
c("A", "A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B",
"B", "C", "C", "C", "C", "C", "C", "C", "C", "C", "D", "D",
"D", "D", "D", "D", "D"),
fixed = TRUE)
expect_equal(
xpectr::smpl(summ_class_level[["Measure"]], n = 30),
c("Median", "SD", "Max", "Min", "NAs", "INFs", "All_1", "Mean",
"Median", "SD", "IQR", "NAs", "INFs", "All_0", "Mean", "Median",
"SD", "IQR", "Max", "Min", "NAs", "All_0", "All_1", "Mean",
"Median", "Max", "Min", "NAs", "INFs", "All_1"),
fixed = TRUE)
expect_equal(
xpectr::smpl(summ_class_level[["Balanced Accuracy"]], n = 30),
c(0.49444, 0.05787, 0.56667, 0.38889, 0, 0, 0.5, 0.53444, 0.54444,
0.05429, 0.09167, 0, 0, 0.5, 0.50889, 0.48889, 0.03621, 0.04444,
0.57778, 0.47778, 0, 0.5, 0.5, 0.53111, 0.52778, 0.64444, 0.41111,
0, 0, 0.5),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Accuracy"]], n = 30),
c(0.63333, 0.03518, 0.66667, 0.55, 0, 0, 0.25, 0.645, 0.65, 0.03932,
0.04167, 0, 0, 0.75, 0.63333, 0.63333, 0.03685, 0.04167, 0.7,
0.58333, 0, 0.75, 0.25, 0.64333, 0.63333, 0.76667, 0.55, 0,
0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["F1"]], n = 30),
c(0.21825, 0.10232, 0.36364, 0.06897, 0, 0, 0.4, 0.3005, 0.33272,
0.09092, 0.12679, 0, 0, NaN, 0.26065, 0.24621, 0.0561, 0.06845,
0.35714, 0.2069, 0, NaN, 0.4, 0.30387, 0.30777, 0.46154, 0.12903,
0, 0, 0.4),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Sensitivity"]], n = 30),
c(0.2, 0.1178, 0.4, 0.06667, 0, 0, 1, 0.31333, 0.33333, 0.1178,
0.16667, 0, 0, 0, 0.26, 0.26667, 0.0663, 0.06667, 0.4, 0.2,
0, 0, 1, 0.30667, 0.33333, 0.4, 0.13333, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Specificity"]], n = 30),
c(0.77778, 0.03506, 0.8, 0.71111, 0, 0, 0, 0.75556, 0.75556, 0.05342,
0.08889, 0, 0, 1, 0.75778, 0.76667, 0.04964, 0.08333, 0.82222,
0.68889, 0, 1, 0, 0.75556, 0.72222, 0.88889, 0.66667, 0, 0,
0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Pos Pred Value"]], n = 30),
c(0.24038, 0.09222, 0.33333, 0.07143, 0, 0, 0.25, 0.29479, 0.30625,
0.07518, 0.12693, 0, 0, NaN, 0.26524, 0.23529, 0.05801, 0.06857,
0.38462, 0.21429, 0, NaN, 0.25, 0.30858, 0.29706, 0.54545, 0.125,
0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Neg Pred Value"]], n = 30),
c(0.74734, 0.02882, 0.78571, 0.69565, 0, 0, NaN, 0.76841, 0.77386,
0.02903, 0.04358, 0, 0, 0.75, 0.75435, 0.74468, 0.01809, 0.02144,
0.78723, 0.7381, 0, 0.75, NaN, 0.76468, 0.76467, 0.81633, 0.70455,
0, 0, NaN),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Kappa"]], n = 30),
c(-0.01163, 0.11716, 0.125, -0.22727, 0, 0, 0, 0.06521, 0.08348,
0.10383, 0.17704, 0, 0, 0, 0.01839, -0.02128, 0.07333, 0.09468,
0.16279, -0.04545, 0, 0, 0, 0.06647, 0.05691, 0.31707, -0.17391,
0, 0, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Detection Rate"]], n = 30),
c(0.05, 0.02945, 0.1, 0.01667, 0, 0, 0.25, 0.07833, 0.08333, 0.02945,
0.04167, 0, 0, 0, 0.065, 0.06667, 0.01657, 0.01667, 0.1, 0.05,
0, 0, 0.25, 0.07667, 0.08333, 0.1, 0.03333, 0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Detection Prevalence"]], n = 30),
c(0.21667, 0.04335, 0.31667, 0.18333, 0, 0, 1, 0.26167, 0.26667,
0.05829, 0.075, 0, 0, 0, 0.24667, 0.225, 0.04431, 0.06667, 0.31667,
0.2, 0, 0, 1, 0.26, 0.275, 0.31667, 0.16667, 0, 0, 1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Prevalence"]], n = 30),
c(0.25, 0, 0.25, 0.25, 0, 0, 0.25, 0.25, 0.25, 0, 0, 0, 0, 0.25,
0.25, 0.25, 0, 0, 0.25, 0.25, 0, 0.25, 0.25, 0.25, 0.25, 0.25,
0.25, 0, 0, 0.25),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Neg Rate"]], n = 30),
c(0.8, 0.1178, 0.93333, 0.6, 0, 0, 0, 0.68667, 0.66667, 0.1178,
0.16667, 0, 0, 1, 0.74, 0.73333, 0.0663, 0.06667, 0.8, 0.6,
0, 1, 0, 0.69333, 0.66667, 0.86667, 0.6, 0, 0, 0),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Pos Rate"]], n = 30),
c(0.22222, 0.03506, 0.28889, 0.2, 0, 0, 1, 0.24444, 0.24444, 0.05342,
0.08889, 0, 0, 0, 0.24222, 0.23333, 0.04964, 0.08333, 0.31111,
0.17778, 0, 0, 1, 0.24444, 0.27778, 0.33333, 0.11111, 0, 0,
1),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Discovery Rate"]], n = 30),
c(0.75962, 0.09222, 0.92857, 0.66667, 0, 0, 0.75, 0.70521, 0.69375,
0.07518, 0.12693, 0, 0, NaN, 0.73476, 0.76471, 0.05801, 0.06857,
0.78571, 0.61538, 0, NaN, 0.75, 0.69142, 0.70294, 0.875, 0.45455,
0, 0, 0.75),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["False Omission Rate"]], n = 30),
c(0.25266, 0.02882, 0.30435, 0.21429, 0, 0, NaN, 0.23159, 0.22614,
0.02903, 0.04358, 0, 0, 0.25, 0.24565, 0.25532, 0.01809, 0.02144,
0.2619, 0.21277, 0, 0.25, NaN, 0.23532, 0.23533, 0.29545, 0.18367,
0, 0, NaN),
tolerance = 1e-4)
expect_equal(
xpectr::smpl(summ_class_level[["Threat Score"]], n = 30),
c(0.1225, 0.06455, 0.22222, 0.03571, 0, 0, 0.25, 0.17984, 0.1996,
0.06288, 0.08689, 0, 0, 0, 0.15096, 0.14039, 0.03816, 0.0449,
0.21739, 0.11538, 0, 0, 0.25, 0.18274, 0.18188, 0.3, 0.06897,
0, 0, 0.25),
tolerance = 1e-4)
# Testing column names
expect_equal(
names(summ_class_level),
c("Class", "Measure", "Balanced Accuracy", "Accuracy", "F1", "Sensitivity",
"Specificity", "Pos Pred Value", "Neg Pred Value", "Kappa",
"Detection Rate", "Detection Prevalence", "Prevalence", "False Neg Rate",
"False Pos Rate", "False Discovery Rate", "False Omission Rate",
"Threat Score"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::element_classes(summ_class_level),
c("character", "character", "numeric", "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(summ_class_level),
c("character", "character", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(summ_class_level),
c(40L, 18L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(summ_class_level)),
character(0),
fixed = TRUE)
## Finished testing 'summ_class_level' ####
## Testing 'multinom_bsl$random_evaluations' ####
## Initially generated by xpectr
xpectr::set_test_seed(42)
# Assigning output
output_19148 <- multinom_bsl$random_evaluations
# Testing class
expect_equal(
class(output_19148),
c("tbl_df", "tbl", "data.frame"),
fixed = TRUE)
# Testing column values
expect_equal(
output_19148[["Repetition"]],
c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
tolerance = 1e-4)
expect_equal(
output_19148[["Overall Accuracy"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Balanced Accuracy"]],
c(0.47778, 0.45556, 0.54444, 0.48889, 0.56667, 0.5, 0.52222, 0.5,
0.52222, 0.58889),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Balanced Accuracy"]],
c(0.47778, 0.45556, 0.54444, 0.48889, 0.56667, 0.5, 0.52222, 0.5,
0.52222, 0.58889),
tolerance = 1e-4)
expect_equal(
output_19148[["Accuracy"]],
c(0.60833, 0.59167, 0.65833, 0.61667, 0.675, 0.625, 0.64167, 0.625,
0.64167, 0.69167),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Accuracy"]],
c(0.60833, 0.59167, 0.65833, 0.61667, 0.675, 0.625, 0.64167, 0.625,
0.64167, 0.69167),
tolerance = 1e-4)
expect_equal(
output_19148[["F1"]],
c(0.21634, 0.17845, 0.3107, 0.22481, 0.35094, 0.24923, 0.28131,
0.24516, 0.2831, 0.37897),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted F1"]],
c(0.21634, 0.17845, 0.3107, 0.22481, 0.35094, 0.24923, 0.28131,
0.24516, 0.2831, 0.37897),
tolerance = 1e-4)
expect_equal(
output_19148[["Sensitivity"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Sensitivity"]],
c(0.21667, 0.18333, 0.31667, 0.23333, 0.35, 0.25, 0.28333, 0.25,
0.28333, 0.38333),
tolerance = 1e-4)
expect_equal(
output_19148[["Specificity"]],
c(0.73889, 0.72778, 0.77222, 0.74444, 0.78333, 0.75, 0.76111, 0.75,
0.76111, 0.79444),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Specificity"]],
c(0.73889, 0.72778, 0.77222, 0.74444, 0.78333, 0.75, 0.76111, 0.75,
0.76111, 0.79444),
tolerance = 1e-4)
expect_equal(
output_19148[["Pos Pred Value"]],
c(0.2197, 0.17897, 0.31667, 0.2211, 0.36124, 0.25015, 0.28078, 0.24494,
0.28692, 0.3837),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Pos Pred Value"]],
c(0.2197, 0.17897, 0.31667, 0.2211, 0.36124, 0.25015, 0.28078, 0.24494,
0.28692, 0.3837),
tolerance = 1e-4)
expect_equal(
output_19148[["Neg Pred Value"]],
c(0.73842, 0.72796, 0.77289, 0.74575, 0.78305, 0.74992, 0.76143,
0.75058, 0.76083, 0.79555),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Neg Pred Value"]],
c(0.73842, 0.72796, 0.77289, 0.74575, 0.78305, 0.74992, 0.76143,
0.75058, 0.76083, 0.79555),
tolerance = 1e-4)
expect_equal(
output_19148[["AUC"]],
c(0.46704, 0.50222, 0.53333, 0.50259, 0.53778, 0.44815, 0.55222,
0.48556, 0.49556, 0.54704),
tolerance = 1e-4)
expect_equal(
output_19148[["Kappa"]],
c(-0.04286, -0.09015, 0.08756, -0.02743, 0.1362, 0.00018, 0.04325,
-0.00217, 0.0458, 0.17601),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Kappa"]],
c(-0.04286, -0.09015, 0.08756, -0.02743, 0.1362, 0.00018, 0.04325,
-0.00217, 0.0458, 0.17601),
tolerance = 1e-4)
expect_equal(
output_19148[["MCC"]],
c(-0.04469, -0.08962, 0.09003, -0.02238, 0.13433, 0, 0.04453, 0,
0.04466, 0.17938),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Rate"]],
c(0.05417, 0.04583, 0.07917, 0.05833, 0.0875, 0.0625, 0.07083, 0.0625,
0.07083, 0.09583),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Detection Rate"]],
c(0.05417, 0.04583, 0.07917, 0.05833, 0.0875, 0.0625, 0.07083, 0.0625,
0.07083, 0.09583),
tolerance = 1e-4)
expect_equal(
output_19148[["Detection Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Detection Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Prevalence"]],
c(0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25),
tolerance = 1e-4)
expect_equal(
output_19148[["False Neg Rate"]],
c(0.78333, 0.81667, 0.68333, 0.76667, 0.65, 0.75, 0.71667, 0.75,
0.71667, 0.61667),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Neg Rate"]],
c(0.78333, 0.81667, 0.68333, 0.76667, 0.65, 0.75, 0.71667, 0.75,
0.71667, 0.61667),
tolerance = 1e-4)
expect_equal(
output_19148[["False Pos Rate"]],
c(0.26111, 0.27222, 0.22778, 0.25556, 0.21667, 0.25, 0.23889, 0.25,
0.23889, 0.20556),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Pos Rate"]],
c(0.26111, 0.27222, 0.22778, 0.25556, 0.21667, 0.25, 0.23889, 0.25,
0.23889, 0.20556),
tolerance = 1e-4)
expect_equal(
output_19148[["False Discovery Rate"]],
c(0.7803, 0.82103, 0.68333, 0.7789, 0.63876, 0.74985, 0.71922, 0.75506,
0.71308, 0.6163),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Discovery Rate"]],
c(0.7803, 0.82103, 0.68333, 0.7789, 0.63876, 0.74985, 0.71922, 0.75506,
0.71308, 0.6163),
tolerance = 1e-4)
expect_equal(
output_19148[["False Omission Rate"]],
c(0.26158, 0.27204, 0.22711, 0.25425, 0.21695, 0.25008, 0.23857,
0.24942, 0.23917, 0.20445),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted False Omission Rate"]],
c(0.26158, 0.27204, 0.22711, 0.25425, 0.21695, 0.25008, 0.23857,
0.24942, 0.23917, 0.20445),
tolerance = 1e-4)
expect_equal(
output_19148[["Threat Score"]],
c(0.12137, 0.09963, 0.18485, 0.13015, 0.21626, 0.14333, 0.16437,
0.14373, 0.16595, 0.23562),
tolerance = 1e-4)
expect_equal(
output_19148[["Weighted Threat Score"]],
c(0.12137, 0.09963, 0.18485, 0.13015, 0.21626, 0.14333, 0.16437,
0.14373, 0.16595, 0.23562),
tolerance = 1e-4)
expect_equal(
output_19148[["Dependent"]],
c("Target", "Target", "Target", "Target", "Target", "Target", "Target",
"Target", "Target", "Target"),
fixed = TRUE)
# Testing column names
expect_equal(
names(output_19148),
c("Repetition", "Overall Accuracy", "Balanced Accuracy", "Weighted Balanced Accuracy",
"Accuracy", "Weighted Accuracy", "F1", "Weighted F1", "Sensitivity",
"Weighted Sensitivity", "Specificity", "Weighted Specificity",
"Pos Pred Value", "Weighted Pos Pred Value", "Neg Pred Value",
"Weighted Neg Pred Value", "AUC", "Kappa", "Weighted Kappa",
"MCC", "Detection Rate", "Weighted Detection Rate", "Detection Prevalence",
"Weighted Detection Prevalence", "Prevalence", "Weighted Prevalence",
"False Neg Rate", "Weighted False Neg Rate", "False Pos Rate",
"Weighted False Pos Rate", "False Discovery Rate", "Weighted False Discovery Rate",
"False Omission Rate", "Weighted False Omission Rate", "Threat Score",
"Weighted Threat Score", "Predictions", "ROC", "Confusion Matrix",
"Class Level Results", "Process", "Dependent"),
fixed = TRUE)
# Testing column classes
expect_equal(
xpectr::smpl(xpectr::element_classes(output_19148), n = 30),
c("numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric", "numeric", "numeric", "numeric",
"numeric", ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "list",
ifelse(is_dplyr_1(), "vctrs_list_of", "list"),
ifelse(is_dplyr_1(), "vctrs_list_of", "list"), "character"),
fixed = TRUE)
# Testing column types
expect_equal(
xpectr::smpl(xpectr::element_types(output_19148), n = 30),
c("double", "double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"double", "double", "double", "double", "double", "double",
"list", "list", "list", "list", "character"),
fixed = TRUE)
# Testing dimensions
expect_equal(
dim(output_19148),
c(10L, 42L))
# Testing group keys
expect_equal(
colnames(dplyr::group_keys(output_19148)),
character(0),
fixed = TRUE)
## Finished testing 'multinom_bsl$random_evaluations' ####
})
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