[CI] Improve R linter script (#5944)
* [CI] Move lint to a separate script * [CI] Improved lintr launcher * Add lintr as a separate action * Add custom parsing logic to print out logs * Fix lintr issues in demos * Run R demos * Fix CRAN checks * Install XGBoost into R env before running lintr * Install devtools (needed to run demos)
This commit is contained in:
Philip Hyunsu Cho
GitHub
parent
8943eb4314
commit
5879acde9a
51
.github/workflows/main.yml
vendored
51
.github/workflows/main.yml
vendored
@ -6,6 +6,9 @@ name: XGBoost-CI
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# events but only for the master branch
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on: [push, pull_request]
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env:
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R_PACKAGES: c('XML', 'igraph', 'data.table', 'magrittr', 'stringi', 'ggplot2', 'DiagrammeR', 'Ckmeans.1d.dp', 'vcd', 'testthat', 'lintr', 'knitr', 'rmarkdown', 'e1071', 'cplm', 'devtools')
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# A workflow run is made up of one or more jobs that can run sequentially or in parallel
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jobs:
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test-with-jvm:
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@ -38,6 +41,49 @@ jobs:
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mvn test -pl :xgboost4j_2.12
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lintr:
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runs-on: ${{ matrix.config.os }}
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name: Run R linters on OS ${{ matrix.config.os }}, R ${{ matrix.config.r }}, Compiler ${{ matrix.config.compiler }}, Build ${{ matrix.config.build }}
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strategy:
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matrix:
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config:
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- {os: windows-latest, r: 'release', compiler: 'mingw', build: 'autotools'}
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env:
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R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
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RSPM: ${{ matrix.config.rspm }}
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steps:
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- uses: actions/checkout@v2
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with:
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submodules: 'true'
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- uses: r-lib/actions/setup-r@master
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with:
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r-version: ${{ matrix.config.r }}
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- name: Cache R packages
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uses: actions/cache@v2
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with:
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-r-${{ matrix.config.r }}-1-${{ hashFiles('R-package/DESCRIPTION') }}
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restore-keys: ${{ runner.os }}-r-${{ matrix.config.r }}-2-
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- name: Install dependencies
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shell: Rscript {0}
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run: |
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install.packages(${{ env.R_PACKAGES }},
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repos = 'http://cloud.r-project.org',
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dependencies = c('Depends', 'Imports', 'LinkingTo'))
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- name: Run lintr
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run: |
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cd R-package
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R.exe CMD INSTALL .
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Rscript.exe tests/run_lint.R
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test-with-R:
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runs-on: ${{ matrix.config.os }}
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@ -78,8 +124,9 @@ jobs:
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- name: Install dependencies
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shell: Rscript {0}
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run: |
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install.packages(c('XML','igraph'))
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install.packages(c('data.table','magrittr','stringi','ggplot2','DiagrammeR','Ckmeans.1d.dp','vcd','testthat','lintr','knitr','rmarkdown'))
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install.packages(${{ env.R_PACKAGES }},
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repos = 'http://cloud.r-project.org',
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dependencies = c('Depends', 'Imports', 'LinkingTo'))
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- uses: actions/setup-python@v2
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with:
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@ -54,7 +54,8 @@ Suggests:
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lintr,
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igraph (>= 1.0.1),
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jsonlite,
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float
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float,
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crayon
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Depends:
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R (>= 3.3.0)
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Imports:
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@ -61,7 +61,7 @@ pred2 <- predict(bst2, test$data)
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print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred))))
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# save model to R's raw vector
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raw = xgb.save.raw(bst)
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raw <- xgb.save.raw(bst)
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# load binary model to R
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bst3 <- xgb.load(raw)
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pred3 <- predict(bst3, test$data)
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@ -93,13 +93,13 @@ dtrain2 <- xgb.DMatrix("dtrain.buffer")
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bst <- xgb.train(data = dtrain2, max_depth = 2, eta = 1, nrounds = 2, watchlist = watchlist,
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nthread = 2, objective = "binary:logistic")
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# information can be extracted from xgb.DMatrix using getinfo
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label = getinfo(dtest, "label")
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label <- getinfo(dtest, "label")
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pred <- predict(bst, dtest)
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err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label)
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print(paste("test-error=", err))
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# You can dump the tree you learned using xgb.dump into a text file
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dump_path = file.path(tempdir(), 'dump.raw.txt')
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dump_path <- file.path(tempdir(), 'dump.raw.txt')
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xgb.dump(bst, dump_path, with_stats = TRUE)
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# Finally, you can check which features are the most important.
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@ -52,7 +52,7 @@ print(levels(df[,Treatment]))
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#
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# Formulae Improved~.-1 used below means transform all categorical features but column Improved to binary values.
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# Column Improved is excluded because it will be our output column, the one we want to predict.
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sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
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sparse_matrix <- sparse.model.matrix(Improved ~ . - 1, data = df)
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cat("Encoding of the sparse Matrix\n")
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print(sparse_matrix)
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@ -61,7 +61,7 @@ print(sparse_matrix)
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# 1. Set, for all rows, field in Y column to 0;
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# 2. set Y to 1 when Improved == Marked;
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# 3. Return Y column
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output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
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output_vector <- df[, Y := 0][Improved == "Marked", Y := 1][, Y]
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# Following is the same process as other demo
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cat("Learning...\n")
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@ -31,4 +31,3 @@ bst <- xgb.train(param, dtrain, num_round, watchlist)
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ypred <- predict(bst, dtest)
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labels <- getinfo(dtest, 'label')
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cat('error of preds=', mean(as.numeric(ypred > 0.5) != labels), '\n')
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@ -5,7 +5,9 @@ set.seed(1024)
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# Function to obtain a list of interactions fitted in trees, requires input of maximum depth
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treeInteractions <- function(input_tree, input_max_depth) {
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trees <- copy(input_tree) # copy tree input to prevent overwriting
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ID_merge <- i.id <- i.feature <- NULL # Suppress warning "no visible binding for global variable"
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trees <- data.table::copy(input_tree) # copy tree input to prevent overwriting
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if (input_max_depth < 2) return(list()) # no interactions if max depth < 2
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if (nrow(input_tree) == 1) return(list())
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@ -15,22 +17,25 @@ treeInteractions <- function(input_tree, input_max_depth){
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parents_left <- trees[!is.na(Split), list(i.id = ID, i.feature = Feature, ID_merge = Yes)]
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parents_right <- trees[!is.na(Split), list(i.id = ID, i.feature = Feature, ID_merge = No)]
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setorderv(trees, 'ID_merge')
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setorderv(parents_left, 'ID_merge')
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setorderv(parents_right, 'ID_merge')
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data.table::setorderv(trees, 'ID_merge')
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data.table::setorderv(parents_left, 'ID_merge')
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data.table::setorderv(parents_right, 'ID_merge')
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trees <- merge(trees, parents_left, by = 'ID_merge', all.x = TRUE)
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trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
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trees[!is.na(i.id), c(paste0('parent_', i - 1), paste0('parent_feat_', i - 1))
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:= list(i.id, i.feature)]
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trees[, c('i.id', 'i.feature') := NULL]
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trees <- merge(trees, parents_right, by = 'ID_merge', all.x = TRUE)
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trees[!is.na(i.id), c(paste0('parent_', i-1), paste0('parent_feat_', i-1)):=list(i.id, i.feature)]
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trees[!is.na(i.id), c(paste0('parent_', i - 1), paste0('parent_feat_', i - 1))
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:= list(i.id, i.feature)]
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trees[, c('i.id', 'i.feature') := NULL]
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}
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# Extract nodes with interactions
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interaction_trees <- trees[!is.na(Split) & !is.na(parent_1),
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c('Feature',paste0('parent_feat_',1:(input_max_depth-1))), with=FALSE]
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c('Feature', paste0('parent_feat_', 1:(input_max_depth - 1))),
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with = FALSE]
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interaction_trees_split <- split(interaction_trees, 1:nrow(interaction_trees))
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interaction_list <- lapply(interaction_trees_split, as.character)
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@ -48,13 +53,14 @@ treeInteractions <- function(input_tree, input_max_depth){
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# Generate sample data
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x <- list()
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for (i in 1:10) {
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x[[i]] = i*rnorm(1000, 10)
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x[[i]] <- i * rnorm(1000, 10)
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}
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x <- as.data.table(x)
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y = -1*x[, rowSums(.SD)] + x[['V1']]*x[['V2']] + x[['V3']]*x[['V4']]*x[['V5']] + rnorm(1000, 0.001) + 3*sin(x[['V7']])
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y <- -1 * x[, rowSums(.SD)] + x[['V1']] * x[['V2']] + x[['V3']] * x[['V4']] * x[['V5']]
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+ rnorm(1000, 0.001) + 3 * sin(x[['V7']])
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train = as.matrix(x)
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train <- as.matrix(x)
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# Interaction constraint list (column names form)
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interaction_list <- list(c('V1', 'V2'), c('V3', 'V4', 'V5'))
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@ -65,31 +71,33 @@ cols2ids <- function(object, col_names) {
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names(LUT) <- col_names
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rapply(object, function(x) LUT[x], classes = "character", how = "replace")
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}
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interaction_list_fid = cols2ids(interaction_list, colnames(train))
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interaction_list_fid <- cols2ids(interaction_list, colnames(train))
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# Fit model with interaction constraints
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bst = xgboost(data = train, label = y, max_depth = 4,
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bst <- xgboost(data = train, label = y, max_depth = 4,
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eta = 0.1, nthread = 2, nrounds = 1000,
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interaction_constraints = interaction_list_fid)
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bst_tree <- xgb.model.dt.tree(colnames(train), bst)
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bst_interactions <- treeInteractions(bst_tree, 4) # interactions constrained to combinations of V1*V2 and V3*V4*V5
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bst_interactions <- treeInteractions(bst_tree, 4)
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# interactions constrained to combinations of V1*V2 and V3*V4*V5
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# Fit model without interaction constraints
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bst2 = xgboost(data = train, label = y, max_depth = 4,
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bst2 <- xgboost(data = train, label = y, max_depth = 4,
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eta = 0.1, nthread = 2, nrounds = 1000)
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bst2_tree <- xgb.model.dt.tree(colnames(train), bst2)
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bst2_interactions <- treeInteractions(bst2_tree, 4) # much more interactions
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# Fit model with both interaction and monotonicity constraints
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bst3 = xgboost(data = train, label = y, max_depth = 4,
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bst3 <- xgboost(data = train, label = y, max_depth = 4,
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eta = 0.1, nthread = 2, nrounds = 1000,
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interaction_constraints = interaction_list_fid,
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monotone_constraints = c(-1, 0, 0, 0, 0, 0, 0, 0, 0, 0))
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bst3_tree <- xgb.model.dt.tree(colnames(train), bst3)
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bst3_interactions <- treeInteractions(bst3_tree, 4) # interactions still constrained to combinations of V1*V2 and V3*V4*V5
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bst3_interactions <- treeInteractions(bst3_tree, 4)
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# interactions still constrained to combinations of V1*V2 and V3*V4*V5
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# Show monotonic constraints still apply by checking scores after incrementing V1
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x1 <- sort(unique(x[['V1']]))
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@ -1,7 +1,6 @@
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data(mtcars)
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head(mtcars)
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bst = xgboost(data=as.matrix(mtcars[,-11]),label=mtcars[,11],
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bst <- xgboost(data = as.matrix(mtcars[, -11]), label = mtcars[, 11],
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objective = 'count:poisson', nrounds = 5)
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pred = predict(bst,as.matrix(mtcars[,-11]))
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pred <- predict(bst, as.matrix(mtcars[, -11]))
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sqrt(mean((pred - mtcars[, 11]) ^ 2))
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@ -7,17 +7,17 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
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watchlist <- list(eval = dtest, train = dtrain)
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nrounds = 2
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nrounds <- 2
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# training the model for two rounds
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bst = xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
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bst <- xgb.train(param, dtrain, nrounds, nthread = 2, watchlist)
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cat('start testing prediction from first n trees\n')
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labels <- getinfo(dtest, 'label')
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### predict using first 1 tree
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ypred1 = predict(bst, dtest, ntreelimit=1)
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ypred1 <- predict(bst, dtest, ntreelimit = 1)
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# by default, we predict using all the trees
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ypred2 = predict(bst, dtest)
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ypred2 <- predict(bst, dtest)
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cat('error of ypred1=', mean(as.numeric(ypred1 > 0.5) != labels), '\n')
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cat('error of ypred2=', mean(as.numeric(ypred2 > 0.5) != labels), '\n')
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@ -11,17 +11,17 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
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param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic')
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nrounds = 4
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nrounds <- 4
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# training the model for two rounds
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bst = xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
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bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds, nthread = 2)
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# Model accuracy without new features
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accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
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accuracy.before <- (sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label)
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/ length(agaricus.test$label))
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# by default, we predict using all the trees
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pred_with_leaf = predict(bst, dtest, predleaf = TRUE)
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pred_with_leaf <- predict(bst, dtest, predleaf = TRUE)
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head(pred_with_leaf)
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create.new.tree.features <- function(model, original.features){
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@ -47,7 +47,9 @@ watchlist <- list(train = new.dtrain)
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bst <- xgb.train(params = param, data = new.dtrain, nrounds = nrounds, nthread = 2)
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# Model accuracy with new features
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accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
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accuracy.after <- (sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label)
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/ length(agaricus.test$label))
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# Here the accuracy was already good and is now perfect.
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cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
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cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
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accuracy.after, "!\n"))
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@ -1,14 +1,14 @@
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# running all scripts in demo folder
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demo(basic_walkthrough)
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demo(custom_objective)
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demo(boost_from_prediction)
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demo(predict_first_ntree)
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demo(generalized_linear_model)
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demo(cross_validation)
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demo(create_sparse_matrix)
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demo(predict_leaf_indices)
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demo(early_stopping)
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demo(poisson_regression)
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demo(caret_wrapper)
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demo(tweedie_regression)
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#demo(gpu_accelerated) # can only run when built with GPU support
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demo(basic_walkthrough, package = 'xgboost')
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demo(custom_objective, package = 'xgboost')
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demo(boost_from_prediction, package = 'xgboost')
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demo(predict_first_ntree, package = 'xgboost')
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demo(generalized_linear_model, package = 'xgboost')
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demo(cross_validation, package = 'xgboost')
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demo(create_sparse_matrix, package = 'xgboost')
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demo(predict_leaf_indices, package = 'xgboost')
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demo(early_stopping, package = 'xgboost')
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demo(poisson_regression, package = 'xgboost')
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demo(caret_wrapper, package = 'xgboost')
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demo(tweedie_regression, package = 'xgboost')
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#demo(gpu_accelerated, package = 'xgboost') # can only run when built with GPU support
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0
R-package/demo/tweedie_regression.R
Executable file → Normal file
0
R-package/demo/tweedie_regression.R
Executable file → Normal file
71
R-package/tests/run_lint.R
Normal file
71
R-package/tests/run_lint.R
Normal file
@ -0,0 +1,71 @@
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library(lintr)
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library(crayon)
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my_linters <- list(
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absolute_path_linter = lintr::absolute_path_linter,
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assignment_linter = lintr::assignment_linter,
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closed_curly_linter = lintr::closed_curly_linter,
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commas_linter = lintr::commas_linter,
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# commented_code_linter = lintr::commented_code_linter,
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infix_spaces_linter = lintr::infix_spaces_linter,
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line_length_linter = lintr::line_length_linter,
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no_tab_linter = lintr::no_tab_linter,
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object_usage_linter = lintr::object_usage_linter,
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# snake_case_linter = lintr::snake_case_linter,
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# multiple_dots_linter = lintr::multiple_dots_linter,
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object_length_linter = lintr::object_length_linter,
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open_curly_linter = lintr::open_curly_linter,
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# single_quotes_linter = lintr::single_quotes_linter,
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spaces_inside_linter = lintr::spaces_inside_linter,
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spaces_left_parentheses_linter = lintr::spaces_left_parentheses_linter,
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trailing_blank_lines_linter = lintr::trailing_blank_lines_linter,
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trailing_whitespace_linter = lintr::trailing_whitespace_linter,
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true_false = lintr::T_and_F_symbol_linter
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)
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results <- lapply(
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list.files(path = '.', pattern = '\\.[Rr]$', recursive = TRUE),
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function (r_file) {
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cat(sprintf("Processing %s ...\n", r_file))
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list(r_file = r_file,
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output = lintr::lint(filename = r_file, linters = my_linters))
|
||||
})
|
||||
num_issue <- Reduce(sum, lapply(results, function (e) length(e$output)))
|
||||
|
||||
lint2str <- function(lint_entry) {
|
||||
color <- function(type) {
|
||||
switch(type,
|
||||
"warning" = crayon::magenta,
|
||||
"error" = crayon::red,
|
||||
"style" = crayon::blue,
|
||||
crayon::bold
|
||||
)
|
||||
}
|
||||
|
||||
paste0(
|
||||
lapply(lint_entry$output,
|
||||
function (lint_line) {
|
||||
paste0(
|
||||
crayon::bold(lint_entry$r_file, ":",
|
||||
as.character(lint_line$line_number), ":",
|
||||
as.character(lint_line$column_number), ": ", sep = ""),
|
||||
color(lint_line$type)(lint_line$type, ": ", sep = ""),
|
||||
crayon::bold(lint_line$message), "\n",
|
||||
lint_line$line, "\n",
|
||||
lintr:::highlight_string(lint_line$message, lint_line$column_number, lint_line$ranges),
|
||||
"\n",
|
||||
collapse = "")
|
||||
}),
|
||||
collapse = "")
|
||||
}
|
||||
|
||||
if (num_issue > 0) {
|
||||
cat(sprintf('R linters found %d issues:\n', num_issue))
|
||||
for (entry in results) {
|
||||
if (length(entry$output)) {
|
||||
cat(paste0('**** ', crayon::bold(entry$r_file), '\n'))
|
||||
cat(paste0(lint2str(entry), collapse = ''))
|
||||
}
|
||||
}
|
||||
quit(save = 'no', status = 1) # Signal error to parent shell
|
||||
}
|
||||
@ -1,26 +0,0 @@
|
||||
context("Code is of high quality and lint free")
|
||||
test_that("Code Lint", {
|
||||
skip_on_cran()
|
||||
my_linters <- list(
|
||||
absolute_path_linter = lintr::absolute_path_linter,
|
||||
assignment_linter = lintr::assignment_linter,
|
||||
closed_curly_linter = lintr::closed_curly_linter,
|
||||
commas_linter = lintr::commas_linter,
|
||||
# commented_code_linter = lintr::commented_code_linter,
|
||||
infix_spaces_linter = lintr::infix_spaces_linter,
|
||||
line_length_linter = lintr::line_length_linter,
|
||||
no_tab_linter = lintr::no_tab_linter,
|
||||
object_usage_linter = lintr::object_usage_linter,
|
||||
# snake_case_linter = lintr::snake_case_linter,
|
||||
# multiple_dots_linter = lintr::multiple_dots_linter,
|
||||
object_length_linter = lintr::object_length_linter,
|
||||
open_curly_linter = lintr::open_curly_linter,
|
||||
# single_quotes_linter = lintr::single_quotes_linter,
|
||||
spaces_inside_linter = lintr::spaces_inside_linter,
|
||||
spaces_left_parentheses_linter = lintr::spaces_left_parentheses_linter,
|
||||
trailing_blank_lines_linter = lintr::trailing_blank_lines_linter,
|
||||
trailing_whitespace_linter = lintr::trailing_whitespace_linter,
|
||||
true_false = lintr::T_and_F_symbol_linter
|
||||
)
|
||||
lintr::expect_lint_free(linters = my_linters) # uncomment this if you want to check code quality
|
||||
})
|
||||
@ -7,8 +7,8 @@ context("Models from previous versions of XGBoost can be loaded")
|
||||
metadata <- model_generator_metadata()
|
||||
|
||||
run_model_param_check <- function (config) {
|
||||
expect_equal(config$learner$learner_model_param$num_feature, '4')
|
||||
expect_equal(config$learner$learner_train_param$booster, 'gbtree')
|
||||
testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')
|
||||
testthat::expect_equal(config$learner$learner_train_param$booster, 'gbtree')
|
||||
}
|
||||
|
||||
get_num_tree <- function (booster) {
|
||||
@ -27,22 +27,24 @@ run_booster_check <- function (booster, name) {
|
||||
config <- jsonlite::fromJSON(xgb.config(booster))
|
||||
run_model_param_check(config)
|
||||
if (name == 'cls') {
|
||||
expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds * metadata$kClasses)
|
||||
expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
|
||||
expect_equal(config$learner$learner_train_param$objective, 'multi:softmax')
|
||||
expect_equal(as.numeric(config$learner$learner_model_param$num_class), metadata$kClasses)
|
||||
testthat::expect_equal(get_num_tree(booster),
|
||||
metadata$kForests * metadata$kRounds * metadata$kClasses)
|
||||
testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
|
||||
testthat::expect_equal(config$learner$learner_train_param$objective, 'multi:softmax')
|
||||
testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class),
|
||||
metadata$kClasses)
|
||||
} else if (name == 'logit') {
|
||||
expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
|
||||
expect_equal(config$learner$learner_train_param$objective, 'binary:logistic')
|
||||
testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
|
||||
testthat::expect_equal(config$learner$learner_train_param$objective, 'binary:logistic')
|
||||
} else if (name == 'ltr') {
|
||||
expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
expect_equal(config$learner$learner_train_param$objective, 'rank:ndcg')
|
||||
testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
testthat::expect_equal(config$learner$learner_train_param$objective, 'rank:ndcg')
|
||||
} else {
|
||||
expect_equal(name, 'reg')
|
||||
expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
|
||||
expect_equal(config$learner$learner_train_param$objective, 'reg:squarederror')
|
||||
testthat::expect_equal(name, 'reg')
|
||||
testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
|
||||
testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
|
||||
testthat::expect_equal(config$learner$learner_train_param$objective, 'reg:squarederror')
|
||||
}
|
||||
}
|
||||
|
||||
@ -73,5 +75,4 @@ test_that("Models from previous versions of XGBoost can be loaded", {
|
||||
predict(booster, newdata = pred_data)
|
||||
run_booster_check(booster, name)
|
||||
})
|
||||
expect_true(TRUE)
|
||||
})
|
||||
|
||||
@ -46,6 +46,10 @@ def test_with_autotools(args):
|
||||
'R.exe', '-q', '-e',
|
||||
"library(testthat); setwd('tests'); source('testthat.R')"
|
||||
])
|
||||
subprocess.check_call([
|
||||
'R.exe', '-q', '-e',
|
||||
"demo(runall, package = 'xgboost')"
|
||||
])
|
||||
|
||||
|
||||
def test_with_cmake(args):
|
||||
@ -79,6 +83,10 @@ def test_with_cmake(args):
|
||||
'R.exe', '-q', '-e',
|
||||
"library(testthat); setwd('tests'); source('testthat.R')"
|
||||
])
|
||||
subprocess.check_call([
|
||||
'R.exe', '-q', '-e',
|
||||
"demo(runall, package = 'xgboost')"
|
||||
])
|
||||
|
||||
|
||||
def main(args):
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user