[R] Use inplace predict (#9829)

---------

Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>

R-package/tests/testthat/test_basic.R

@@ -139,8 +139,8 @@ test_that("dart prediction works", {
   pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, iterationrange = c(1, nrounds))
   pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
 
-  expect_true(all(matrix(pred_by_train_0, byrow = TRUE) == matrix(pred_by_xgboost_0, byrow = TRUE)))
-  expect_true(all(matrix(pred_by_train_1, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
+  expect_equal(pred_by_train_0, pred_by_xgboost_0, tolerance = 1e-6)
+  expect_equal(pred_by_train_1, pred_by_xgboost_1, tolerance = 1e-6)
   expect_true(all(matrix(pred_by_train_2, byrow = TRUE) == matrix(pred_by_xgboost_2, byrow = TRUE)))
 })
 
@@ -651,6 +651,51 @@ test_that("Can use ranking objectives with either 'qid' or 'group'", {
   expect_equal(pred_qid, pred_gr)
 })
 
+test_that("Can predict on data.frame objects", {
+  data("mtcars")
+  y <- mtcars$mpg
+  x_df <- mtcars[, -1]
+  x_mat <- as.matrix(x_df)
+  dm <- xgb.DMatrix(x_mat, label = y, nthread = n_threads)
+  model <- xgb.train(
+    params = list(
+      tree_method = "hist",
+      objective = "reg:squarederror",
+      nthread = n_threads
+    ),
+    data = dm,
+    nrounds = 5
+  )
+
+  pred_mat <- predict(model, xgb.DMatrix(x_mat), nthread = n_threads)
+  pred_df <- predict(model, x_df, nthread = n_threads)
+  expect_equal(pred_mat, pred_df)
+})
+
+test_that("'base_margin' gives the same result in DMatrix as in inplace_predict", {
+  data("mtcars")
+  y <- mtcars$mpg
+  x <- as.matrix(mtcars[, -1])
+  dm <- xgb.DMatrix(x, label = y, nthread = n_threads)
+  model <- xgb.train(
+    params = list(
+      tree_method = "hist",
+      objective = "reg:squarederror",
+      nthread = n_threads
+    ),
+    data = dm,
+    nrounds = 5
+  )
+
+  set.seed(123)
+  base_margin <- rnorm(nrow(x))
+  dm_w_base <- xgb.DMatrix(data = x, base_margin = base_margin)
+  pred_from_dm <- predict(model, dm_w_base)
+  pred_from_mat <- predict(model, x, base_margin = base_margin)
+
+  expect_equal(pred_from_dm, pred_from_mat)
+})
+
 test_that("Coefficients from gblinear have the expected shape and names", {
   # Single-column coefficients
   data(mtcars)

R-package/tests/testthat/test_dmatrix.R

@@ -302,6 +302,37 @@ test_that("xgb.DMatrix: Inf as missing", {
   file.remove(fname_nan)
 })
 
+test_that("xgb.DMatrix: missing in CSR", {
+  x_dense <- matrix(as.numeric(1:10), nrow = 5)
+  x_dense[2, 1] <- NA_real_
+
+  x_csr <- as(x_dense, "RsparseMatrix")
+
+  m_dense <- xgb.DMatrix(x_dense, nthread = n_threads, missing = NA_real_)
+  xgb.DMatrix.save(m_dense, "dense.dmatrix")
+
+  m_csr <- xgb.DMatrix(x_csr, nthread = n_threads, missing = NA)
+  xgb.DMatrix.save(m_csr, "csr.dmatrix")
+
+  denseconn <- file("dense.dmatrix", "rb")
+  csrconn <- file("csr.dmatrix", "rb")
+
+  expect_equal(file.size("dense.dmatrix"), file.size("csr.dmatrix"))
+
+  bytes <- file.size("dense.dmatrix")
+  densedmatrix <- readBin(denseconn, "raw", n = bytes)
+  csrmatrix <- readBin(csrconn, "raw", n = bytes)
+
+  expect_equal(length(densedmatrix), length(csrmatrix))
+  expect_equal(densedmatrix, csrmatrix)
+
+  close(denseconn)
+  close(csrconn)
+
+  file.remove("dense.dmatrix")
+  file.remove("csr.dmatrix")
+})
+
 test_that("xgb.DMatrix: error on three-dimensional array", {
   set.seed(123)
   x <- matrix(rnorm(500), nrow = 50)