avoid using task$data twice

R/LearnerRegrXgboost.R

@@ -201,17 +201,17 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
 
       data = task$data(cols = task$feature_names)
       target = task$data(cols = task$target_names)
-      data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = data.matrix(target))
+      xgb_data = xgboost::xgb.DMatrix(data = as_numeric_matrix(data), label = data.matrix(target))
 
       if ("weights" %in% task$properties) {
-        xgboost::setinfo(data, "weight", task$weights$weight)
+        xgboost::setinfo(xgb_data, "weight", task$weights$weight)
       }
 
       bm = pv$base_margin
       pv$base_margin = NULL # silence xgb.train message
       bm_is_feature = !is.null(bm) && is.character(bm) && (bm %in% task$feature_names)
       if (bm_is_feature) {
-        xgboost::setinfo(data, "base_margin", task$data(cols = bm)[[1L]])
+        xgboost::setinfo(xgb_data, "base_margin", data[[bm]])
       }
 
       # the last element in the watchlist is used as the early stopping set
@@ -222,11 +222,11 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
       if (!is.null(internal_valid_task)) {
         test_data = internal_valid_task$data(cols = task$feature_names)
         test_target = internal_valid_task$data(cols = task$target_names)
-        test_data = xgboost::xgb.DMatrix(data = as_numeric_matrix(test_data), label = data.matrix(test_target))
+        xgb_test_data = xgboost::xgb.DMatrix(data = as_numeric_matrix(test_data), label = data.matrix(test_target))
         if (bm_is_feature) {
-          xgboost::setinfo(test_data, "base_margin", internal_valid_task$data(cols = bm)[[1L]])
+          xgboost::setinfo(xgb_test_data, "base_margin", test_data[[bm]])
         }
-        pv$watchlist = c(pv$watchlist, list(test = test_data))
+        pv$watchlist = c(pv$watchlist, list(test = xgb_test_data))
       }
 
       # set internal validation measure
@@ -246,7 +246,7 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
         pv$maximize = !measure$minimize
       }
 
-      invoke(xgboost::xgb.train, data = data, .args = pv)
+      invoke(xgboost::xgb.train, data = xgb_data, .args = pv)
     },
     #' Returns the `$best_iteration` when early stopping is activated.
     .predict = function(task) {