update R documentation

R-package/R/lgb.Booster.R

@@ -708,8 +708,8 @@ Booster <- R6::R6Class(
 #' If None or <= 0, starts from the first iteration.
 #' @param num_iteration int or None, optional (default=None)
 #' Limit number of iterations in the prediction.
-#' If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used;
-#' otherwise, all iterations from start_iteration are used.
+#' If None, if the best iteration exists and start_iteration is None or <= 0, the
+#' best iteration is used; otherwise, all iterations from start_iteration are used.
 #' If <= 0, all iterations from start_iteration are used (no limits).
 #' @param rawscore whether the prediction should be returned in the for of original untransformed
 #' sum of predictions from boosting iterations' results. E.g., setting \code{rawscore=TRUE}

R-package/tests/testthat/test_Predictor.R

@@ -41,31 +41,39 @@ test_that("start_iteration works correctly", {
  , nrounds = 100L
  , objective = "binary"
  , save_name = tempfile(fileext = ".model")
- , valids=list("test" = dtest)
- , early_stopping_rounds=2L
+ , valids = list("test" = dtest)
+ , early_stopping_rounds = 2L
  )
  expect_true(lgb.is.Booster(bst))
- pred1 <- predict(bst, data=test$data, rawscore=TRUE)
- pred_contrib1 <- predict(bst, test$data, predcontrib=TRUE)
+ pred1 <- predict(bst, data = test$data, rawscore = TRUE)
+ pred_contrib1 <- predict(bst, test$data, predcontrib = TRUE)
  pred2 <- rep(0.0, length(pred1))
  pred_contrib2 <- rep(0.0, length(pred2))
  step <- 11L
  end_iter <- 99L
- if (bst$best_iter != -1) {
- end_iter <- bst$best_iter - 1
+ if (bst$best_iter != -1L) {
+ end_iter <- bst$best_iter - 1L
  }
- start_iters <- seq(0L, end_iter, by=step)
+ start_iters <- seq(0L, end_iter, by = step)
  for (start_iter in start_iters) {
- n_iter = min(c(end_iter - start_iter + 1, step))
- inc_pred <- predict(bst, test$data, start_iteration=start_iter, num_iteration=n_iter, rawscore=TRUE)
- inc_pred_contrib <- bst$predict(test$data, start_iteration=start_iter, num_iteration=n_iter, predcontrib=TRUE)
+ n_iter <- min(c(end_iter - start_iter + 1L, step))
+ inc_pred <- predict(bst, test$data
+ , start_iteration = start_iter
+ , num_iteration = n_iter
+ , rawscore = TRUE
+ )
+ inc_pred_contrib <- bst$predict(test$data
+ , start_iteration = start_iter
+ , num_iteration = n_iter
+ , predcontrib = TRUE
+ )
  pred2 <- pred2 + inc_pred
  pred_contrib2 <- pred_contrib2 + inc_pred_contrib
  }
  expect_equal(pred2, pred1)
  expect_equal(pred_contrib2, pred_contrib1)
 
- pred_leaf1 <- predict(bst, test$data, predleaf=TRUE)
- pred_leaf2 <- predict(bst, test$data, start_iteration=0L, num_iteration=end_iter + 1, predleaf=TRUE)
+ pred_leaf1 <- predict(bst, test$data, predleaf = TRUE)
+ pred_leaf2 <- predict(bst, test$data, start_iteration = 0L, num_iteration = end_iter + 1L, predleaf = TRUE)
  expect_equal(pred_leaf1, pred_leaf2)
 })

src/boosting/gbdt.h

@@ -361,8 +361,7 @@ class GBDT : public GBDTBase {
  start_iteration = std::min(start_iteration, num_iteration_for_pred_);
  if (num_iteration > 0) {
  num_iteration_for_pred_ = std::min(num_iteration, num_iteration_for_pred_ - start_iteration);
- }
- else {
+ } else {
  num_iteration_for_pred_ = num_iteration_for_pred_ - start_iteration;
  }
  start_iteration_for_pred_ = start_iteration;