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DS-4360 chore, Improve tests slightly
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jrwishart committed Mar 31, 2023
1 parent bb834df commit 9398e2a
Showing 1 changed file with 65 additions and 44 deletions.
109 changes: 65 additions & 44 deletions tests/testthat/test-CART.R
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,13 @@ test_that("saving variables", {
z <- CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM, data = bank,
subset = bank$ID > 100)
expect_error(predict(z), NA)
expect_error(flipData::Probabilities(z))
expect_error(flipData::Probabilities(z),
"Probabilities not available for numeric dependent variables.",
fixed = TRUE)

z <- suppressWarnings(CART(fOverall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100))
expect_warning(z <- CART(fOverall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100),
"Some categories do not appear in the data: 'fOverall: 8'")
expect_error(predict(z), NA)
expect_error(flipData::Probabilities(z), NA)
expect_error(print(z), NA)
Expand All @@ -24,45 +27,54 @@ test_that("saving variables", {


z <- CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM, data = bank, subset = bank$ID > 100)
test_that("rpart prediction",{
test_that("rpart prediction", {
expect_equal(unname(predict(z)[1]), 4.258064516129032)
})


z <- suppressWarnings(CART(fOverall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100))
test_that("rpart Probabilities", {
expect_warning(z <- CART(fOverall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100),
"Some categories do not appear in the data: 'fOverall: 8'")
expect_equal(unname(flipData::Probabilities(z)[1, 4]), 0.2444444444444445)
})

z <- suppressWarnings(CART(fOverall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100))

# Reading in the libraries so that their outputs do not pollute the test results.
library(mice)
library(hot.deck)

test_that("Error if missing data", {
type <- "Sankey"
exp.error <- paste0("The data contains missing values. Change the 'missing' ",
"option to run the analysis.")
# Changing data
expect_error((CART(yesno ~ crl.tot + dollar + bang + money + n000 + make,
data = spam.sample, missing = "Error if missing data")),NA)
expect_silent(CART(yesno ~ crl.tot + dollar + bang + money + n000 + make,
data = spam.sample, missing = "Error if missing data"))
colas$Q32[unclass(colas$Q32) == 1] <- NA
expect_that((CART(Q32 ~ Q2, data = colas, subset = TRUE, missing = "Error if missing data")),
(throws_error()))
expect_that((CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = NULL, output = type, missing = "Error if missing data")), (throws_error()))
expect_error(CART(Q32 ~ Q2, data = colas, subset = TRUE, missing = "Error if missing data"),
exp.error, fixed = TRUE)
expect_error(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = NULL, output = type,
missing = "Error if missing data"),
exp.error, fixed = TRUE)
# filter
expect_that((CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = NULL, output = type, missing = "Error if missing data")), (throws_error()))
expect_error(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = NULL,
output = type, missing = "Error if missing data"),
exp.error, fixed = TRUE)
# weight
expect_that((CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = bank$ID, output = type, missing = "Error if missing data")), (throws_error()))
expect_error(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = bank$ID, output = type,
missing = "Error if missing data"),
exp.error, fixed = TRUE)
# weight and filter
expect_that((CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = bank$ID, missing = "Error if missing")), (throws_error()))
expect_error(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = bank$ID,
missing = "Error if missing data"),
exp.error, fixed = TRUE)
# DS-1525, subset creates empty level of outcome
expect_error(suppressWarnings(CART(Q32 ~ Q2 + Q3, data = colas, subset = colas$Q32 != "Don't know")), NA)
expect_warning(CART(Q32 ~ Q2 + Q3, data = colas, subset = colas$Q32 != "Don't know"),
"Some categories do not appear in the data: 'Q32. Income")
})


Expand All @@ -72,10 +84,11 @@ for (missing in c("Exclude cases with missing data",
for (type in c("Sankey", "Tree", "Text", "Prediction-Accuracy Table", "Cross Validation"))
test_that(paste(missing, type),
{
exclude <- missing == "Exclude cases with missing data"
imputation <- missing == "Imputation (replace missing values with estimates)"
expect_error((z <- suppressWarnings(CART(yesno ~ crl.tot + dollar + bang + money + n000 + make,
data = spam.sample, subset = TRUE, weights = NULL,
output = type, missing = missing))), NA)
expect_error(z <- CART(yesno ~ crl.tot + dollar + bang + money + n000 + make,
data = spam.sample, subset = TRUE, weights = NULL,
output = type, missing = missing), NA)

if (type == "Prediction-Accuracy Table")
expect_equal(attr(z, "ChartData"), ExtractChartData(z$confusion))
Expand All @@ -87,33 +100,41 @@ for (missing in c("Exclude cases with missing data",
colas$Q32[unclass(colas$Q32) == 1] <- NA
colas.small <- colas[, colnames(colas) %in% c("Q32", "Q3", "Q2", "Q4_A", "Q4_B", "Q4_C", "Q11", "Q12")]
colas.small$Q3[1] <- NA
expect_error((suppressWarnings(CART(Q32 ~ Q3, data = colas.small, subset = TRUE,
weights = NULL, output = type, missing = missing))), NA)
expect_error((suppressWarnings(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = NULL, output = type, missing = missing))), NA)
no.cat.warn <- "Some categories do not appear in the data"
exclude.warn <- r"(^\d{2}\% of the data is missing)"
expect_warning(CART(Q32 ~ Q3, data = colas.small, subset = TRUE,
weights = NULL, output = type, missing = missing),
if (!imputation) no.cat.warn else NA)
expect_warning(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = NULL, output = type,
missing = missing),
if (exclude) exclude.warn else NA)
# filter
expect_error((suppressWarnings(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = NULL, output = type,
missing = missing))), NA)
expect_warning(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = NULL, output = type,
missing = missing),
if (exclude) exclude.warn else NA)
# weight
expect_error((suppressWarnings(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = bank$ID, output = type,
missing = missing))), NA)
expect_warning(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = TRUE, weights = bank$ID, output = type,
missing = missing),
if (exclude) exclude.warn else NA)
# weight and filter
expect_error((suppressWarnings(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = bank$ID,
output = type, missing = missing))), NA)
expect_warning(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM,
data = bank, subset = bank$ID > 100, weights = bank$ID,
output = type, missing = missing),
if (exclude) exclude.warn else NA)
})


for (pruning in c("None", "Minimum error", "Smallest tree"))
for (stopping in c(TRUE, FALSE))
test_that(paste(missing, type),
{
expect_error((suppressWarnings(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM, data = bank,
subset = bank$ID > 100, weights = bank$ID,
output = "Sankey", missing = "Exclude cases with missing data",
prune = pruning, early.stopping = stopping))), NA)
test_that(paste(missing, type), {
expect_warning(CART(Overall ~ Fees + Interest + Phone + Branch + Online + ATM, data = bank,
subset = bank$ID > 100, weights = bank$ID,
output = "Sankey", missing = "Exclude cases with missing data",
prune = pruning, early.stopping = stopping),
r"(^\d{2}\% of the data is missing)")
})


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