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[CI] Improve R linter script (#5944)

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* [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
2020-07-27 00:55:35 -07:00
committed by GitHub
parent 8943eb4314
commit 5879acde9a
22 changed files with 317 additions and 207 deletions
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26
R-package/tests/testthat/test_lint.R
View File

@@ -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
})

33
R-package/tests/testthat/test_model_compatibility.R
View File

@@ -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)
})