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[R] Rename BIAS -> (Intercept) #10337

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May 30, 2024
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[R] Rename BIAS -> (Intercept) #10337

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4 changes: 2 additions & 2 deletions R-package/R/xgb.Booster.R
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Original file line number Diff line number Diff line change
Expand Up @@ -249,7 +249,7 @@ xgb.get.handle <- function(object) {
#' summary(rowSums(pred_contr) - qlogis(pred))
#' # for the 1st record, let's inspect its features that had non-zero contribution to prediction:
#' contr1 <- pred_contr[1,]
#' contr1 <- contr1[-length(contr1)] # drop BIAS
#' contr1 <- contr1[-length(contr1)] # drop intercept
#' contr1 <- contr1[contr1 != 0] # drop non-contributing features
#' contr1 <- contr1[order(abs(contr1))] # order by contribution magnitude
#' old_mar <- par("mar")
Expand Down Expand Up @@ -473,7 +473,7 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA

.Call(XGSetArrayDimInplace_R, arr, rev(shape))

cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "(Intercept)") else NULL
n_groups <- shape[2]

## Needed regardless of whether strict shape is being used.
Expand Down
2 changes: 1 addition & 1 deletion R-package/man/predict.xgb.Booster.Rd
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10 changes: 5 additions & 5 deletions R-package/tests/testthat/test_helpers.R
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Expand Up @@ -101,7 +101,7 @@ test_that("predict feature contributions works", {
# gbtree binary classifier
expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE), regexp = NA)
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "(Intercept)"))
pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
# must work with data that has no column names
Expand All @@ -114,14 +114,14 @@ test_that("predict feature contributions works", {
# gbtree binary classifier (approximate method)
expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE, approxcontrib = TRUE), regexp = NA)
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "(Intercept)"))
pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)

# gblinear binary classifier
expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "(Intercept)"))
pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-5)
# manual calculation of linear terms
Expand All @@ -137,7 +137,7 @@ test_that("predict feature contributions works", {
expect_is(pred_contr, "list")
expect_length(pred_contr, 3)
for (g in seq_along(pred_contr)) {
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "(Intercept)"))
expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 1e-5)
}

Expand All @@ -151,7 +151,7 @@ test_that("predict feature contributions works", {
byrow = TRUE
)
for (g in seq_along(pred_contr)) {
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "(Intercept)"))
expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), float_tolerance)
# manual calculation of linear terms
coefs <- c(coefs_all[-1, g], coefs_all[1, g]) # intercept needs to be the last
Expand Down
4 changes: 2 additions & 2 deletions R-package/tests/testthat/test_interactions.R
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Expand Up @@ -48,7 +48,7 @@ test_that("predict feature interactions works", {
intr <- predict(b, dm, predinteraction = TRUE)
expect_equal(dim(intr), c(N, P + 1, P + 1))
# check assigned colnames
cn <- c(letters[1:P], "BIAS")
cn <- c(letters[1:P], "(Intercept)")
expect_equal(dimnames(intr), list(NULL, cn, cn))

# check the symmetry
Expand All @@ -60,7 +60,7 @@ test_that("predict feature interactions works", {
# diagonal terms for features 3,4,5 must be close to zero
expect_lt(Reduce(max, sapply(3:P, function(i) max(abs(intr[, i, i])))), 0.05)

# BIAS must have no interactions
# Intercept must have no interactions
expect_lt(max(abs(intr[, 1:P, P + 1])), 0.00001)

# interactions other than 2 x 3 must be close to zero
Expand Down
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