[R] rename Quality -> Gain (#9938)

R-package/R/xgb.model.dt.tree.R

@@ -28,7 +28,7 @@
 #' - `Yes`: ID of the next node when the split condition is met.
 #' - `No`: ID of the next node when the split condition is not met.
 #' - `Missing`: ID of the next node when the branch value is missing.
-#' - `Quality`: either the split gain (change in loss) or the leaf value.
+#' - `Gain`: either the split gain (change in loss) or the leaf value.
 #' - `Cover`: metric related to the number of observations either seen by a split
 #'            or collected by a leaf during training.
 #'
@@ -122,7 +122,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
   # parse branch lines
   branch_rx <- paste0("f(\\d+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
                       "gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
-  branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
+  branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Gain", "Cover")
   td[
     isLeaf == FALSE,
     (branch_cols) := {
@@ -132,7 +132,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
       xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
       if (length(xtr) == 0) {
         as.data.table(
-          list(Feature = "NA", Split = "NA", Yes = "NA", No = "NA", Missing = "NA", Quality = "NA", Cover = "NA")
+          list(Feature = "NA", Split = "NA", Yes = "NA", No = "NA", Missing = "NA", Gain = "NA", Cover = "NA")
         )
       } else {
         as.data.table(xtr)
@@ -152,7 +152,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
 
   # parse leaf lines
   leaf_rx <- paste0("leaf=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
-  leaf_cols <- c("Feature", "Quality", "Cover")
+  leaf_cols <- c("Feature", "Gain", "Cover")
   td[
     isLeaf == TRUE,
     (leaf_cols) := {
@@ -167,7 +167,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
   ]
 
   # convert some columns to numeric
-  numeric_cols <- c("Split", "Quality", "Cover")
+  numeric_cols <- c("Split", "Gain", "Cover")
   td[, (numeric_cols) := lapply(.SD, as.numeric), .SDcols = numeric_cols]
   if (use_int_id) {
     int_cols <- c("Yes", "No", "Missing")

R-package/R/xgb.plot.deepness.R

@@ -92,7 +92,7 @@ xgb.plot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.d
     stop("Model tree columns are not as expected!\n",
          "  Note that this function works only for tree models.")
 
-  dt_depths <- merge(get.leaf.depth(dt_tree), dt_tree[, .(ID, Cover, Weight = Quality)], by = "ID")
+  dt_depths <- merge(get.leaf.depth(dt_tree), dt_tree[, .(ID, Cover, Weight = Gain)], by = "ID")
   setkeyv(dt_depths, c("Tree", "ID"))
   # count by depth levels, and also calculate average cover at a depth
   dt_summaries <- dt_depths[, .(.N, Cover = mean(Cover)), Depth]
@@ -157,6 +157,6 @@ get.leaf.depth <- function(dt_tree) {
 # They are mainly column names inferred by Data.table...
 globalVariables(
   c(
-    ".N", "N", "Depth", "Quality", "Cover", "Tree", "ID", "Yes", "No", "Feature", "Leaf", "Weight"
+    ".N", "N", "Depth", "Gain", "Cover", "Tree", "ID", "Yes", "No", "Feature", "Leaf", "Weight"
   )
 )

R-package/R/xgb.plot.multi.trees.R

@@ -95,13 +95,13 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5,
     data.table::set(tree.matrix, j = nm, value = sub("^\\d+-", "", tree.matrix[[nm]]))
 
   nodes.dt <- tree.matrix[
-        , .(Quality = sum(Quality))
+        , .(Gain = sum(Gain))
         , by = .(abs.node.position, Feature)
       ][, .(Text = paste0(
               paste0(
                 Feature[seq_len(min(length(Feature), features_keep))],
                 " (",
-                format(Quality[seq_len(min(length(Quality), features_keep))], digits = 5),
+                format(Gain[seq_len(min(length(Gain), features_keep))], digits = 5),
                 ")"
               ),
               collapse = "\n"

R-package/R/xgb.plot.tree.R

@@ -132,7 +132,7 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
 
   dt <- xgb.model.dt.tree(feature_names = feature_names, model = model, trees = trees)
 
-  dt[, label := paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Quality)]
+  dt[, label := paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Gain)]
   if (show_node_id)
     dt[, label := paste0(ID, ": ", label)]
   dt[Node == 0, label := paste0("Tree ", Tree, "\n", label)]
@@ -199,4 +199,4 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c("Feature", "ID", "Cover", "Quality", "Split", "Yes", "No", "Missing", ".", "shape", "filledcolor", "label"))
+globalVariables(c("Feature", "ID", "Cover", "Gain", "Split", "Yes", "No", "Missing", ".", "shape", "filledcolor", "label"))

R-package/tests/testthat/test_helpers.R

@@ -275,7 +275,7 @@ test_that("xgb.Booster serializing as R object works", {
 
 test_that("xgb.model.dt.tree works with and without feature names", {
   .skip_if_vcd_not_available()
-  names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
+  names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Gain", "Cover")
   dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
   expect_equal(names.dt.trees, names(dt.tree))
   if (!flag_32bit)
@@ -341,7 +341,7 @@ test_that("xgb.importance works with and without feature names", {
       trees = trees
     )[
       Feature != "Leaf", .(
-        Gain = sum(Quality),
+        Gain = sum(Gain),
         Cover = sum(Cover),
         Frequency = .N
       ),

R-package/tests/testthat/test_update.R

@@ -53,9 +53,9 @@ test_that("updating the model works", {
   # should be the same evaluation but different gains and larger cover
   expect_equal(bst2$evaluation_log, bst2r$evaluation_log)
   if (!win32_flag) {
-    expect_equal(tr2[Feature == 'Leaf']$Quality, tr2r[Feature == 'Leaf']$Quality)
+    expect_equal(tr2[Feature == 'Leaf']$Gain, tr2r[Feature == 'Leaf']$Gain)
   }
-  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2r[Feature != 'Leaf']$Quality)), 100)
+  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Gain - tr2r[Feature != 'Leaf']$Gain)), 100)
   expect_gt(sum(tr2r$Cover) / sum(tr2$Cover), 1.5)
 
   # process type 'update' for no-subsampling model, refreshing the tree stats AND leaves from training data:
@@ -72,8 +72,8 @@ test_that("updating the model works", {
   tr2u <- xgb.model.dt.tree(model = bst2u)
   # should be the same evaluation but different gains and larger cover
   expect_equal(bst2$evaluation_log, bst2u$evaluation_log)
-  expect_equal(tr2[Feature == 'Leaf']$Quality, tr2u[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Quality - tr2u[Feature != 'Leaf']$Quality)), 100)
+  expect_equal(tr2[Feature == 'Leaf']$Gain, tr2u[Feature == 'Leaf']$Gain)
+  expect_gt(sum(abs(tr2[Feature != 'Leaf']$Gain - tr2u[Feature != 'Leaf']$Gain)), 100)
   expect_gt(sum(tr2u$Cover) / sum(tr2$Cover), 1.5)
   # the results should be the same as for the model with an extra 'refresh' updater
   expect_equal(bst2r$evaluation_log, bst2u$evaluation_log)
@@ -87,8 +87,8 @@ test_that("updating the model works", {
   tr1ut <- xgb.model.dt.tree(model = bst1ut)
   # should be the same evaluations but different gains and smaller cover (test data is smaller)
   expect_equal(bst1$evaluation_log, bst1ut$evaluation_log)
-  expect_equal(tr1[Feature == 'Leaf']$Quality, tr1ut[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr1[Feature != 'Leaf']$Quality - tr1ut[Feature != 'Leaf']$Quality)), 100)
+  expect_equal(tr1[Feature == 'Leaf']$Gain, tr1ut[Feature == 'Leaf']$Gain)
+  expect_gt(sum(abs(tr1[Feature != 'Leaf']$Gain - tr1ut[Feature != 'Leaf']$Gain)), 100)
   expect_lt(sum(tr1ut$Cover) / sum(tr1$Cover), 0.5)
 })
 
@@ -111,7 +111,7 @@ test_that("updating works for multiclass & multitree", {
 
   # should be the same evaluation but different gains and larger cover
   expect_equal(bst0$evaluation_log, bst0u$evaluation_log)
-  expect_equal(tr0[Feature == 'Leaf']$Quality, tr0u[Feature == 'Leaf']$Quality)
-  expect_gt(sum(abs(tr0[Feature != 'Leaf']$Quality - tr0u[Feature != 'Leaf']$Quality)), 100)
+  expect_equal(tr0[Feature == 'Leaf']$Gain, tr0u[Feature == 'Leaf']$Gain)
+  expect_gt(sum(abs(tr0[Feature != 'Leaf']$Gain - tr0u[Feature != 'Leaf']$Gain)), 100)
   expect_gt(sum(tr0u$Cover) / sum(tr0$Cover), 1.5)
 })