context("Gradient Boost")
data(hbatwithsplits, package = "flipExampleData")
hair <- hbatwithsplits
hair1 <- flipTransformations::AsNumeric(hair[, paste0("x",6:18)], binary = FALSE, remove.first = TRUE)
hair1$x1 <- hair$x1
hair1$split60 <- hair$split60
hair1$id <- hair$id
hair1$num <- suppressWarnings(flipTransformations::AsNumeric(hair1$x1, binary = FALSE))
hair1$numeric <- hair1$num + runif(length(hair1$num)) / 10
attr(hair1$x7, "question") <- "Variable number 7"
hair1$cat <- factor(hair1$num)
hair1$bin <- hair1$num > 1
# Create a smaller subset of variables for testing dot on RHS
hair2 <- flipTransformations::AsNumeric(hair[, paste0("x",6:18)], binary = FALSE, remove.first = TRUE)
test_that("Print Gradient Boost: outputs and boosters",{
for (output in c("Detail", "Accuracy", "Prediction-Accuracy Table"))
for (booster in c("gbtree", "gblinear"))
for (grid.search in c(TRUE, FALSE))
{
z <- GradientBoost(numeric ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = TRUE, output = output, data = hair1, subset = split60 == "Estimation Sample",
booster = booster, grid.search = grid.search)
expect_error(capture.output(print(z)), NA)
z <- GradientBoost(cat ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = FALSE, output = output, data = hair1, subset = split60 == "Estimation Sample",
booster = booster, grid.search = grid.search)
expect_error(capture.output(print(z)), NA)
}
})
test_that("Print Gradient Boost: binary outcome",{
for (booster in c("gbtree", "gblinear"))
{
z <- GradientBoost(bin ~ x6 + x7 + x8 + x9,
show.labels = FALSE, output = "Accuracy", data = hair1, subset = split60 == "Estimation Sample",
booster = booster, grid.search = FALSE)
expect_error(capture.output(print(z)), NA)
}
})
test_that("Print Gradient Boost Importance",{
for (grid.search in c(TRUE, FALSE))
{
z <- GradientBoost(numeric ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = TRUE, output = "Importance", data = hair1, subset = split60 == "Estimation Sample",
booster = "gbtree", grid.search = grid.search)
expect_error(capture.output(print(z)), NA)
z <- GradientBoost(cat ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = FALSE, output = "Imporance", data = hair1, subset = split60 == "Estimation Sample",
booster = "gbtree", grid.search = grid.search)
expect_error(capture.output(print(z)), NA)
}
})
library(flipRegression)
test_that("Gradient Boost Weights and Filters",{
# no weight, no filter
expect_error(z <- GradientBoost(x1 ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, booster = "gbtree"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Filtered
expect_error(z <- GradientBoost(x1 ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, subset = split60 == "Estimation Sample", booster = "gblinear"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Weighted
expect_error(z <- GradientBoost(x1 ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, weights = hair1$id, booster = "gblinear"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Weighted and filtered
expect_error(z <- GradientBoost(x1 ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, weights = hair1$id, subset = split60 == "Estimation Sample",
booster = "gbtree"), NA)
Accuracy(z)
ConfusionMatrix(z)
## numeric dependent variable
# no weight, no filter
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, booster = "gblinear"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Filtered
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, subset = split60 == "Estimation Sample", booster = "gbtree"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Weighted
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, weights = hair1$id, booster = "gbtree"), NA)
Accuracy(z)
ConfusionMatrix(z)
# Weighted and filtered
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1, weights = hair1$id, subset = split60 == "Estimation Sample",
booster = "gblinear"), NA)
Accuracy(z)
ConfusionMatrix(z)
})
test_that("Gradient Boost Errors",{
# insufficient data
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair1[1:5, ], show.labels = TRUE), "There are fewer observations*")
# importance for linear booster
expect_error(z <- GradientBoost(num ~ x6 + x7 + x8 + x9 + x10 + x11 + x12, data = hair1, booster = "gblinear",
output = "Importance", show.labels = TRUE), "Importance is only available for*")
})
test_that("Save variables",
{
z <- GradientBoost(numeric ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = FALSE, output = "Detail", data = hair1, subset = split60 == "Estimation Sample")
expect_error(predict(z), NA)
expect_equal(length(predict(z)), 100)
expect_error(flipData::Observed(z), NA)
expect_error(flipData::Probabilities(z), "Probabilities are only applicable to models with categorical outcome variables.")
z <- GradientBoost(cat ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
booster = "gbtree", data = hair1, subset = split60 == "Estimation Sample")
expect_error(predict(z), NA)
expect_equal(length(predict(z)), 100)
expect_error(flipData::Observed(z), NA)
expect_error(flipData::Probabilities(z), NA)
z <- GradientBoost(cat ~ x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
booster = "gblinear", data = hair1, subset = split60 == "Estimation Sample")
expect_error(predict(z), NA)
expect_equal(length(predict(z)), 100)
expect_error(flipData::Observed(z), NA)
expect_error(flipData::Probabilities(z), NA)
})
test_that("Gradient Boost: dot in formula",{
z <- GradientBoost(x6 ~ ., data = hair2, booster = "gblinear")
z2 <- GradientBoost(x6 ~ x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
data = hair2, booster = "gblinear")
z$original$call <- z$formula <- NULL
z2$original$call <- z2$formula <- NULL
expect_equal(z, z2)
})
test_that("Gradient Boost: missing data",{
hair2$x6[runif(nrow(hair2)) > 0.8] <- NA
hair2$x7[runif(nrow(hair2)) > 0.8] <- NA
expect_error(GradientBoost(x6 ~ x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = FALSE, output = "Prediction-Accuracy Table",
missing = "Imputation (replace missing values with estimates)", data = hair2), NA)
expect_error(GradientBoost(x6 ~ x7 + x8 + x9 + x10 + x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18,
show.labels = FALSE, output = "Prediction-Accuracy Table",
missing = "Error if missing data", data = hair2), "The data contains missing values.")
})
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