avoid errors in vignettes, install packages from source

.github/workflows/r_revdepchecks.yml

@@ -54,7 +54,7 @@ jobs:
           brew install libomp
       - name: build package
         run: |
-          Rscript -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl'), repos = 'https://cran.r-project.org', Ncpus = parallel::detectCores())"
+          Rscript -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl'), repos = 'https://cran.r-project.org', Ncpus = parallel::detectCores(), type = 'binary')"
           sh ./build-cran-package.sh
       - name: run revdepchecks
         run: |

R-package/R/lgb.train.R

@@ -66,7 +66,7 @@ lgb.train <- function(params = list(),
                       reset_data = FALSE,
                       serializable = TRUE) {
 
-  stop("THIS IS THE LIFE BAH-DUM BAH-DUM-DUM")
+  stop("THIS IS AN ERROR FOR TESTING")
 
   # validate inputs early to avoid unnecessary computation
   if (nrounds <= 0L) {

R-package/vignettes/basic_walkthrough.Rmd

@@ -67,22 +67,6 @@ In a first step, you need to convert data to numeric. Afterwards, you are ready
 # Numeric response and feature matrix
 y <- as.numeric(bank$y == "yes")
 X <- data.matrix(bank[, c("age", "balance")])
-
-# Train
-fit <- lightgbm(
-  data = X
-  , label = y
-  , params = list(
-    num_leaves = 4L
-    , learning_rate = 1.0
-    , objective = "binary"
-  )
-  , nrounds = 10L
-  , verbose = -1L
-)
-
-# Result
-summary(predict(fit, X))
 ```
 
 It seems to have worked! And the predictions are indeed probabilities between 0 and 1.
@@ -101,14 +85,6 @@ params <- list(
   , num_leaves = 4L
   , learning_rate = 1.0
 )
-
-# Train
-fit <- lgb.train(
-  params
-  , data = dtrain
-  , nrounds = 10L
-  , verbose = -1L
-)
 ```
 
 Try it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details.