From 80ff3b089b9066a77a6861cfc1e1072bad61cdfa Mon Sep 17 00:00:00 2001
From: James Lamb <jaylamb20@gmail.com>
Date: Thu, 23 Apr 2020 23:47:34 -0500
Subject: [PATCH] [R-package] added tests on LGBM_BoosterResetTrainingData_R

---
 R-package/tests/testthat/test_lgb.Booster.R | 63 +++++++++++++++++++++
 1 file changed, 63 insertions(+)

diff --git a/R-package/tests/testthat/test_lgb.Booster.R b/R-package/tests/testthat/test_lgb.Booster.R
index fbc84bc4ed46..b1662e413b7b 100644
--- a/R-package/tests/testthat/test_lgb.Booster.R
+++ b/R-package/tests/testthat/test_lgb.Booster.R
@@ -279,3 +279,66 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
     expect_identical(bst_from_ds$eval_train(), list())
     expect_equal(bst_from_ds$current_iter(), nrounds)
 })
+
+test_that("Booster$update() passing a train_set works as expected", {
+    set.seed(708L)
+    data(agaricus.train, package = "lightgbm")
+    nrounds <- 2L
+
+    # train with 2 rounds and then update
+    bst <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds
+        , objective = "binary"
+    )
+    expect_true(lgb.is.Booster(bst))
+    expect_equal(bst$current_iter(), nrounds)
+    bst$update(
+        train_set = Dataset$new(
+            data = agaricus.train$data
+            , label = agaricus.train$label
+        )
+    )
+    expect_true(lgb.is.Booster(bst))
+    expect_equal(bst$current_iter(), nrounds + 1L)
+
+    # train with 3 rounds directlry
+    bst2 <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds +  1L
+        , objective = "binary"
+    )
+    expect_true(lgb.is.Booster(bst2))
+    expect_equal(bst2$current_iter(), nrounds +  1L)
+
+    # model with 2 rounds + 1 update should be identical to 3 rounds
+    expect_equal(bst2$eval_train()[[1L]][["value"]], 0.04806585)
+    expect_equal(bst$eval_train()[[1L]][["value"]], bst2$eval_train()[[1L]][["value"]])
+})
+
+test_that("Booster$update() throws an informative error if you provide a non-Dataset to update()", {
+    set.seed(708L)
+    data(agaricus.train, package = "lightgbm")
+    nrounds <- 2L
+
+    # train with 2 rounds and then update
+    bst <- lightgbm(
+        data = as.matrix(agaricus.train$data)
+        , label = agaricus.train$label
+        , num_leaves = 4L
+        , learning_rate = 1.0
+        , nrounds = nrounds
+        , objective = "binary"
+    )
+    expect_error({
+        bst$update(
+            train_set = data.frame(x = rnorm(10L))
+        )
+    }, regexp = "lgb.Booster.update: Only can use lgb.Dataset", fixed = TRUE)
+})