[R] enable multi-dimensional base_margin (#9885)

R-package/R/xgb.DMatrix.R

@@ -16,6 +16,8 @@
 #' only care about the relative ordering of data points within each group,
 #' so it doesn't make sense to assign weights to individual data points.
 #' @param base_margin Base margin used for boosting from existing model.
+#'
+#'        In the case of multi-output models, one can also pass multi-dimensional base_margin.
 #' @param missing a float value to represents missing values in data (used only when input is a dense matrix).
 #'        It is useful when a 0 or some other extreme value represents missing values in data.
 #' @param silent whether to suppress printing an informational message after loading from a file.
@@ -439,9 +441,7 @@ setinfo.xgb.DMatrix <- function(object, name, info, ...) {
     return(TRUE)
   }
   if (name == "base_margin") {
-    # if (length(info)!=nrow(object))
-    #   stop("The length of base margin must equal to the number of rows in the input data")
-    .Call(XGDMatrixSetInfo_R, object, name, as.numeric(info))
+    .Call(XGDMatrixSetInfo_R, object, name, info)
     return(TRUE)
   }
   if (name == "group") {

R-package/tests/testthat/test_dmatrix.R

@@ -349,3 +349,29 @@ test_that("xgb.DMatrix: data.frame", {
   m <- xgb.DMatrix(df, enable_categorical = TRUE)
   expect_equal(getinfo(m, "feature_type"), c("c", "c"))
 })
+
+test_that("xgb.DMatrix: can take multi-dimensional 'base_margin'", {
+  set.seed(123)
+  x <- matrix(rnorm(100 * 10), nrow = 100)
+  y <- matrix(rnorm(100 * 2), nrow = 100)
+  b <- matrix(rnorm(100 * 2), nrow = 100)
+  model <- xgb.train(
+    data = xgb.DMatrix(data = x, label = y, nthread = n_threads),
+    params = list(
+      objective = "reg:squarederror",
+      tree_method = "hist",
+      multi_strategy = "multi_output_tree",
+      base_score = 0,
+      nthread = n_threads
+    ),
+    nround = 1
+  )
+  pred_only_x <- predict(model, x, nthread = n_threads, reshape = TRUE)
+  pred_w_base <- predict(
+    model,
+    xgb.DMatrix(data = x, base_margin = b, nthread = n_threads),
+    nthread = n_threads,
+    reshape = TRUE
+  )
+  expect_equal(pred_only_x, pred_w_base - b, tolerance = 1e-5)
+})