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Unverified Commit 7dcbb8cd authored by James Lamb's avatar James Lamb Committed by GitHub
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[R-package] limit number of threads used in tests and examples (fixes #5987) (#5988)

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Showing with 257 additions and 38 deletions
R-package/man/lgb.plot.importance.Rd
View file @ 7dcbb8cd
......@@ -47,6 +47,7 @@ params <- list(
, learning_rate = 0.1
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, num_threads = 2L
)
model <- lgb.train(
......
R-package/man/lgb.plot.interpretation.Rd
View file @ 7dcbb8cd
......@@ -58,6 +58,7 @@ params <- list(
, max_depth = -1L
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, num_threads = 2L
)
model <- lgb.train(
params = params
......
R-package/man/lgb.restore_handle.Rd
View file @ 7dcbb8cd
......@@ -34,7 +34,9 @@ model <- lightgbm(
, agaricus.train$label
, params = list(objective = "binary")
, nrounds = 5L
, verbose = 0)
, verbose = 0
, num_threads = 2L
)
fname <- tempfile(fileext="rds")
saveRDS(model, fname)
......
R-package/man/lgb.save.Rd
View file @ 7dcbb8cd
......@@ -33,6 +33,7 @@ params <- list(
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
valids <- list(test = dtest)
model <- lgb.train(
......
R-package/man/lgb.train.Rd
View file @ 7dcbb8cd
......@@ -141,6 +141,7 @@ params <- list(
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
valids <- list(test = dtest)
model <- lgb.train(
......
R-package/man/predict.lgb.Booster.Rd
View file @ 7dcbb8cd
......@@ -132,6 +132,7 @@ params <- list(
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
valids <- list(test = dtest)
model <- lgb.train(
......
R-package/man/readRDS.lgb.Booster.Rd
View file @ 7dcbb8cd
......@@ -34,6 +34,7 @@ params <- list(
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
valids <- list(test = dtest)
model <- lgb.train(
......
R-package/man/saveRDS.lgb.Booster.Rd
View file @ 7dcbb8cd
......@@ -57,6 +57,7 @@ params <- list(
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
valids <- list(test = dtest)
model <- lgb.train(
......
R-package/tests/testthat/helper.R 0 → 100644
View file @ 7dcbb8cd
# ref for this file:
#
# * https://r-pkgs.org/testing-design.html#testthat-setup-files
# LightGBM-internal fix to comply with CRAN policy of only using up to 2 threads in tests and example.
#
# per https://cran.r-project.org/web/packages/policies.html
#
# > If running a package uses multiple threads/cores it must never use more than two simultaneously:
# the check farm is a shared resource and will typically be running many checks simultaneously.
#
.LGB_MAX_THREADS <- 2L
R-package/tests/testthat/test_Predictor.R
View file @ 7dcbb8cd
......@@ -14,6 +14,7 @@ test_that("Predictor$finalize() should not fail", {
data = dtrain
, params = list(
objective = "regression"
, num_threads = .LGB_MAX_THREADS
)
, verbose = VERBOSITY
, nrounds = 3L
......@@ -42,6 +43,7 @@ test_that("predictions do not fail for integer input", {
data = dtrain
, params = list(
objective = "regression"
, num_threads = .LGB_MAX_THREADS
)
, verbose = VERBOSITY
, nrounds = 3L
......@@ -77,6 +79,7 @@ test_that("start_iteration works correctly", {
, learning_rate = 0.6
, objective = "binary"
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 50L
, valids = list("test" = dtest)
......@@ -126,7 +129,7 @@ test_that("Feature contributions from sparse inputs produce sparse outputs", {
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(min_data_in_leaf = 5L)
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)
pred_dense <- predict(bst, X, type = "contrib")
......@@ -157,7 +160,7 @@ test_that("Sparse feature contribution predictions do not take inputs with wrong
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(min_data_in_leaf = 5L)
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)
X_wrong <- X[, c(1L:10L, 1L:10L)]
......@@ -187,7 +190,7 @@ test_that("Feature contribution predictions do not take non-general CSR or CSC i
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(min_data_in_leaf = 5L)
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)
expect_error(predict(bst, SmatC, type = "contrib"))
......@@ -211,6 +214,7 @@ test_that("predict() params should override keyword argument for raw-score predi
objective = "binary"
, min_data_in_leaf = 1L
, seed = 708L
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
......@@ -262,6 +266,7 @@ test_that("predict() params should override keyword argument for leaf-index pred
objective = "regression"
, min_data_in_leaf = 1L
, seed = 708L
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
......@@ -315,6 +320,7 @@ test_that("predict() params should override keyword argument for feature contrib
objective = "regression"
, min_data_in_leaf = 1L
, seed = 708L
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
......@@ -426,7 +432,7 @@ test_that("predict() keeps row names from data (regression)", {
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(min_data_in_leaf = 1L)
, params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)
)
.check_all_row_name_expectations(bst, X)
})
......@@ -442,6 +448,7 @@ test_that("predict() keeps row names from data (binary classification)", {
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
.check_all_row_name_expectations(bst, X)
})
......@@ -455,7 +462,7 @@ test_that("predict() keeps row names from data (multi-class classification)", {
bst <- lgb.train(
data = dtrain
, obj = "multiclass"
, params = list(num_class = 3L)
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
, nrounds = 5L
, verbose = VERBOSITY
)
......@@ -479,7 +486,7 @@ test_that("predictions for regression and binary classification are returned as
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(min_data_in_leaf = 1L)
, params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
expect_true(is.vector(pred))
......@@ -497,6 +504,7 @@ test_that("predictions for regression and binary classification are returned as
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
expect_true(is.vector(pred))
......@@ -516,7 +524,7 @@ test_that("predictions for multiclass classification are returned as matrix", {
, obj = "multiclass"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_class = 3L)
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
expect_true(is.matrix(pred))
......@@ -533,7 +541,7 @@ test_that("Single-row predictions are identical to multi-row ones", {
X <- as.matrix(mtcars[, -1L])
y <- mtcars[, 1L]
dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
params <- list(min_data_in_leaf = 2L)
params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)
model <- lgb.train(
params = params
, data = dtrain
......@@ -594,7 +602,7 @@ test_that("Fast-predict configuration accepts non-default prediction types", {
X <- as.matrix(mtcars[, -1L])
y <- mtcars[, 1L]
dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
params <- list(min_data_in_leaf = 2L)
params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)
model <- lgb.train(
params = params
, data = dtrain
......@@ -624,7 +632,7 @@ test_that("Fast-predict configuration does not block other prediction types", {
X <- as.matrix(mtcars[, -1L])
y <- mtcars[, 1L]
dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))
params <- list(min_data_in_leaf = 2L)
params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)
model <- lgb.train(
params = params
, data = dtrain
......@@ -661,6 +669,7 @@ test_that("predict type='class' returns predicted class for classification objec
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(bst, X, type = "class")
expect_true(all(pred %in% c(0L, 1L)))
......@@ -674,7 +683,7 @@ test_that("predict type='class' returns predicted class for classification objec
, obj = "multiclass"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_class = 3L)
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X, type = "class")
expect_true(all(pred %in% c(0L, 1L, 2L)))
......@@ -690,6 +699,7 @@ test_that("predict type='class' returns values in the target's range for regress
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(bst, X, type = "class")
expect_true(!any(pred %in% c(0.0, 1.0)))
......
R-package/tests/testthat/test_basic.R
View file @ 7dcbb8cd
......@@ -55,18 +55,22 @@ CONSTANT_METRIC_VALUE <- 0.2
DTRAIN_RANDOM_REGRESSION <- lgb.Dataset(
data = as.matrix(rnorm(100L), ncol = 1L, drop = FALSE)
, label = rnorm(100L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
DVALID_RANDOM_REGRESSION <- lgb.Dataset(
data = as.matrix(rnorm(50L), ncol = 1L, drop = FALSE)
, label = rnorm(50L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
DTRAIN_RANDOM_CLASSIFICATION <- lgb.Dataset(
data = as.matrix(rnorm(120L), ncol = 1L, drop = FALSE)
, label = sample(c(0L, 1L), size = 120L, replace = TRUE)
, params = list(num_threads = .LGB_MAX_THREADS)
)
DVALID_RANDOM_CLASSIFICATION <- lgb.Dataset(
data = as.matrix(rnorm(37L), ncol = 1L, drop = FALSE)
, label = sample(c(0L, 1L), size = 37L, replace = TRUE)
, params = list(num_threads = .LGB_MAX_THREADS)
)
test_that("train and predict binary classification", {
......@@ -79,6 +83,7 @@ test_that("train and predict binary classification", {
, objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
, valids = list(
......@@ -120,6 +125,7 @@ test_that("train and predict softmax", {
, metric = "multi_error"
, num_class = 3L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 20L
, valids = list(
......@@ -150,12 +156,14 @@ test_that("use of multiple eval metrics works", {
, objective = "binary"
, metric = metrics
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, valids = list(
"train" = lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
)
)
......@@ -179,6 +187,7 @@ test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expec
, objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -197,6 +206,7 @@ test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expec
, objective = "regression"
, metric = "l2"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -206,7 +216,7 @@ test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expec
test_that("lightgbm() rejects negative or 0 value passed to nrounds", {
dtrain <- lgb.Dataset(train$data, label = train$label)
params <- list(objective = "regression", metric = "l2,l1")
params <- list(objective = "regression", metric = "l2,l1", num_threads = .LGB_MAX_THREADS)
for (nround_value in c(-10L, 0L)) {
expect_error({
bst <- lightgbm(
......@@ -231,6 +241,7 @@ test_that("lightgbm() accepts nrounds as either a top-level argument or paramete
, metric = "l2"
, num_leaves = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -244,6 +255,7 @@ test_that("lightgbm() accepts nrounds as either a top-level argument or paramete
, num_leaves = 5L
, nrounds = nrounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -258,6 +270,7 @@ test_that("lightgbm() accepts nrounds as either a top-level argument or paramete
, num_leaves = 5L
, nrounds = nrounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -285,10 +298,12 @@ test_that("lightgbm() performs evaluation on validation sets if they are provide
dvalid1 <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid2 <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
bst <- lightgbm(
......@@ -302,6 +317,7 @@ test_that("lightgbm() performs evaluation on validation sets if they are provide
, "auc"
)
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
, valids = list(
......@@ -310,6 +326,7 @@ test_that("lightgbm() performs evaluation on validation sets if they are provide
, "train" = lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
)
)
......@@ -334,6 +351,7 @@ test_that("training continuation works", {
train$data
, label = train$label
, free_raw_data = FALSE
, params = list(num_threads = .LGB_MAX_THREADS)
)
watchlist <- list(train = dtrain)
param <- list(
......@@ -342,6 +360,7 @@ test_that("training continuation works", {
, num_leaves = 5L
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
# train for 10 consecutive iterations
......@@ -368,6 +387,7 @@ test_that("cv works", {
, min_data = 1L
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lgb.cv(
params
......@@ -388,6 +408,7 @@ test_that("CVBooster$reset_parameter() works as expected", {
, min_data = 1L
, num_leaves = 7L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = 3L
......@@ -405,11 +426,12 @@ test_that("CVBooster$reset_parameter() works as expected", {
})
test_that("lgb.cv() rejects negative or 0 value passed to nrounds", {
dtrain <- lgb.Dataset(train$data, label = train$label)
dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = 2L))
params <- list(
objective = "regression"
, metric = "l2,l1"
, min_data = 1L
, num_threads = .LGB_MAX_THREADS
)
for (nround_value in c(-10L, 0L)) {
expect_error({
......@@ -453,6 +475,7 @@ test_that("lightgbm.cv() gives the correct best_score and best_iter for a metric
dtrain <- lgb.Dataset(
data = as.matrix(runif(n = 500L, min = 0.0, max = 15.0), drop = FALSE)
, label = rep(c(0L, 1L), 250L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
cv_bst <- lgb.cv(
......@@ -465,6 +488,7 @@ test_that("lightgbm.cv() gives the correct best_score and best_iter for a metric
, learning_rate = 1.5
, num_leaves = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
expect_true(methods::is(cv_bst, "lgb.CVBooster"))
......@@ -487,6 +511,7 @@ test_that("lgb.cv() fit on linearly-relatead data improves when using linear lea
return(lgb.Dataset(
data = X
, label = 2L * X + runif(nrow(X), 0L, 0.1)
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -496,6 +521,7 @@ test_that("lgb.cv() fit on linearly-relatead data improves when using linear lea
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -520,12 +546,13 @@ test_that("lgb.cv() fit on linearly-relatead data improves when using linear lea
})
test_that("lgb.cv() respects showsd argument", {
dtrain <- lgb.Dataset(train$data, label = train$label)
dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))
params <- list(
objective = "regression"
, metric = "l2"
, min_data = 1L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
nrounds <- 5L
set.seed(708L)
......@@ -559,6 +586,7 @@ test_that("lgb.cv() raises an informative error for unrecognized objectives", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
expect_error({
capture.output({
......@@ -567,6 +595,7 @@ test_that("lgb.cv() raises an informative error for unrecognized objectives", {
, params = list(
objective_type = "not_a_real_objective"
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
}, type = "message")
......@@ -579,6 +608,7 @@ test_that("lgb.cv() respects parameter aliases for objective", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
cv_bst <- lgb.cv(
data = dtrain
......@@ -587,6 +617,7 @@ test_that("lgb.cv() respects parameter aliases for objective", {
, application = "binary"
, num_iterations = nrounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nfold = nfold
)
......@@ -602,10 +633,12 @@ test_that("lgb.cv() prefers objective in params to keyword argument", {
data = lgb.Dataset(
data = EuStockMarkets[, c("SMI", "CAC", "FTSE")]
, label = EuStockMarkets[, "DAX"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
, params = list(
application = "regression_l1"
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 5L
, obj = "regression_l2"
......@@ -631,6 +664,7 @@ test_that("lgb.cv() respects parameter aliases for metric", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
cv_bst <- lgb.cv(
data = dtrain
......@@ -640,6 +674,7 @@ test_that("lgb.cv() respects parameter aliases for metric", {
, num_iterations = nrounds
, metric_types = c("auc", "binary_logloss")
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nfold = nfold
)
......@@ -657,6 +692,7 @@ test_that("lgb.cv() respects eval_train_metric argument", {
, metric = "l2"
, min_data = 1L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
nrounds <- 5L
set.seed(708L)
......@@ -696,6 +732,7 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
data = lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
, nrounds = 10L
, params = list(
......@@ -703,11 +740,13 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
, metric = metrics
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, valids = list(
"train" = lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
)
)
......@@ -743,6 +782,7 @@ test_that("lgb.train() respects parameter aliases for objective", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
bst <- lgb.train(
data = dtrain
......@@ -751,6 +791,7 @@ test_that("lgb.train() respects parameter aliases for objective", {
, application = "binary"
, num_iterations = nrounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, valids = list(
"the_training_data" = dtrain
......@@ -767,10 +808,12 @@ test_that("lgb.train() prefers objective in params to keyword argument", {
data = lgb.Dataset(
data = EuStockMarkets[, c("SMI", "CAC", "FTSE")]
, label = EuStockMarkets[, "DAX"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
, params = list(
loss = "regression_l1"
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 5L
, obj = "regression_l2"
......@@ -792,6 +835,7 @@ test_that("lgb.train() respects parameter aliases for metric", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
bst <- lgb.train(
data = dtrain
......@@ -801,6 +845,7 @@ test_that("lgb.train() respects parameter aliases for metric", {
, num_iterations = nrounds
, metric_types = c("auc", "binary_logloss")
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, valids = list(
"train" = dtrain
......@@ -814,11 +859,12 @@ test_that("lgb.train() respects parameter aliases for metric", {
})
test_that("lgb.train() rejects negative or 0 value passed to nrounds", {
dtrain <- lgb.Dataset(train$data, label = train$label)
dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))
params <- list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
for (nround_value in c(-10L, 0L)) {
expect_error({
......@@ -840,6 +886,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
data = lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
, nrounds = nrounds
, params = list(
......@@ -847,6 +894,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
, metric = "l2"
, num_leaves = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -855,6 +903,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
data = lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
, params = list(
objective = "regression"
......@@ -870,6 +919,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
data = lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
, nrounds = 20L
, params = list(
......@@ -878,6 +928,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
, num_leaves = 5L
, nrounds = nrounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -988,12 +1039,14 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
dtrain <- lgb.Dataset(
train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
params <- list(
objective = "binary"
, metric = "binary_error"
, force_col_wise = TRUE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst_col_wise <- lgb.train(
params = params
......@@ -1006,6 +1059,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
, metric = "binary_error"
, force_row_wise = TRUE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst_row_wise <- lgb.train(
params = params
......@@ -1037,6 +1091,7 @@ test_that("lgb.train() works as expected with sparse features", {
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["x"]], drop = FALSE)
, label = trainDF[["y"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 1L
bst <- lgb.train(
......@@ -1045,6 +1100,7 @@ test_that("lgb.train() works as expected with sparse features", {
, min_data = 1L
, min_data_in_bin = 1L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1071,10 +1127,12 @@ test_that("lgb.train() works with early stopping for classification", {
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid <- lgb.Dataset(
data = as.matrix(validDF[["feat1"]], drop = FALSE)
, label = validDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
......@@ -1086,6 +1144,7 @@ test_that("lgb.train() works with early stopping for classification", {
objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1110,6 +1169,7 @@ test_that("lgb.train() works with early stopping for classification", {
, metric = "binary_error"
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1142,10 +1202,12 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid <- lgb.Dataset(
data = as.matrix(validDF[["feat1"]], drop = FALSE)
, label = validDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 5L
......@@ -1159,6 +1221,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1183,6 +1246,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
, metric = "binary_error"
, n_iter_no_change = value
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1204,10 +1268,12 @@ test_that("lgb.train() works with early stopping for classification with a metri
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid <- lgb.Dataset(
data = test$data
, label = test$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
......@@ -1223,6 +1289,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
, max_depth = 3L
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1237,6 +1304,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
, max_depth = 3L
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1281,10 +1349,12 @@ test_that("lgb.train() works with early stopping for regression", {
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid <- lgb.Dataset(
data = as.matrix(validDF[["feat1"]], drop = FALSE)
, label = validDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
......@@ -1296,6 +1366,7 @@ test_that("lgb.train() works with early stopping for regression", {
objective = "regression"
, metric = "rmse"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1320,6 +1391,7 @@ test_that("lgb.train() works with early stopping for regression", {
, metric = "rmse"
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1355,6 +1427,7 @@ test_that("lgb.train() does not stop early if early_stopping_rounds is not given
objective = "regression"
, metric = "None"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds
......@@ -1399,6 +1472,7 @@ test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to
, metric = "None"
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, list(
objective = "regression"
......@@ -1406,6 +1480,7 @@ test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to
, early_stopping_rounds = early_stopping_rounds
, first_metric_only = FALSE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
......@@ -1469,6 +1544,7 @@ test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based
, early_stopping_rounds = early_stopping_rounds
, first_metric_only = TRUE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds
......@@ -1515,6 +1591,7 @@ test_that("lgb.train() works when a mixture of functions and strings are passed
objective = "regression"
, metric = "None"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds
......@@ -1571,6 +1648,7 @@ test_that("lgb.train() works when a list of strings or a character vector is pas
objective = "binary"
, metric = "None"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds
......@@ -1608,6 +1686,7 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri
objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds
......@@ -1640,6 +1719,7 @@ test_that("lgb.train() works when you give a function for eval", {
objective = "binary"
, metric = "None"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds
......@@ -1667,10 +1747,12 @@ test_that("lgb.train() works with early stopping for regression with a metric th
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid <- lgb.Dataset(
data = as.matrix(validDF[["feat1"]], drop = FALSE)
, label = validDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
......@@ -1689,6 +1771,7 @@ test_that("lgb.train() works with early stopping for regression with a metric th
, min_data_in_bin = 5L
, early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -1720,6 +1803,7 @@ test_that("lgb.train() supports non-ASCII feature names", {
dtrain <- lgb.Dataset(
data = matrix(rnorm(400L), ncol = 4L)
, label = rnorm(100L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
# content below is equivalent to
#
......@@ -1740,6 +1824,7 @@ test_that("lgb.train() supports non-ASCII feature names", {
, params = list(
metric = "rmse"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, colnames = feature_names
)
......@@ -1800,6 +1885,7 @@ test_that("lgb.train() updates params based on keyword arguments", {
dtrain <- lgb.Dataset(
data = matrix(rnorm(400L), ncol = 4L)
, label = rnorm(100L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
# defaults from keyword arguments should be used if not specified in params
......@@ -1808,7 +1894,7 @@ test_that("lgb.train() updates params based on keyword arguments", {
bst <- lgb.train(
data = dtrain
, obj = "regression"
, params = list()
, params = list(num_threads = .LGB_MAX_THREADS)
)
})
)
......@@ -1824,6 +1910,7 @@ test_that("lgb.train() updates params based on keyword arguments", {
, params = list(
"verbosity" = 5L
, "num_iterations" = 2L
, num_threads = .LGB_MAX_THREADS
)
)
})
......@@ -1840,6 +1927,7 @@ test_that("lgb.train() updates params based on keyword arguments", {
, params = list(
"verbose" = 5L
, "num_boost_round" = 2L
, num_threads = .LGB_MAX_THREADS
)
)
})
......@@ -1863,14 +1951,17 @@ test_that("when early stopping is not activated, best_iter and best_score come f
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid1 <- lgb.Dataset(
data = as.matrix(validDF[["feat1"]], drop = FALSE)
, label = validDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid2 <- lgb.Dataset(
data = as.matrix(validDF[1L:10L, "feat1"], drop = FALSE)
, label = validDF[1L:10L, "target"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
train_params <- list(
......@@ -1879,6 +1970,7 @@ test_that("when early stopping is not activated, best_iter and best_score come f
, learning_rate = 1.5
, num_leaves = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
# example 1: two valids, neither are the training data
......@@ -2020,10 +2112,12 @@ test_that("lightgbm.train() gives the correct best_score and best_iter for a met
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid1 <- lgb.Dataset(
data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE)
, label = validDF[1L:25L, "target"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
bst <- lgb.train(
......@@ -2039,6 +2133,7 @@ test_that("lightgbm.train() gives the correct best_score and best_iter for a met
, learning_rate = 1.5
, num_leaves = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
# note that "something-random-we-would-not-hardcode" was recognized as the training
......@@ -2070,14 +2165,17 @@ test_that("using lightgbm() without early stopping, best_iter and best_score com
dtrain <- lgb.Dataset(
data = as.matrix(trainDF[["feat1"]], drop = FALSE)
, label = trainDF[["target"]]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid1 <- lgb.Dataset(
data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE)
, label = validDF[1L:25L, "target"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
dvalid2 <- lgb.Dataset(
data = as.matrix(validDF[26L:50L, "feat1"], drop = FALSE)
, label = validDF[26L:50L, "target"]
, params = list(num_threads = .LGB_MAX_THREADS)
)
nrounds <- 10L
bst <- lightgbm(
......@@ -2093,6 +2191,7 @@ test_that("using lightgbm() without early stopping, best_iter and best_score com
, metric = "auc"
, learning_rate = 1.5
, num_leaves = 5L
, num_threads = .LGB_MAX_THREADS
)
, verbose = -7L
)
......@@ -2120,6 +2219,7 @@ test_that("lgb.cv() works when you specify both 'metric' and 'eval' with strings
objective = "binary"
, metric = "binary_error"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds
......@@ -2154,6 +2254,7 @@ test_that("lgb.cv() works when you give a function for eval", {
objective = "binary"
, metric = "None"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_CLASSIFICATION
, nfold = nfolds
......@@ -2180,6 +2281,7 @@ test_that("If first_metric_only is TRUE, lgb.cv() decides to stop early based on
, early_stopping_rounds = early_stopping_rounds
, first_metric_only = TRUE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_REGRESSION
, nfold = nfolds
......@@ -2237,6 +2339,7 @@ test_that("early stopping works with lgb.cv()", {
, early_stopping_rounds = early_stopping_rounds
, first_metric_only = TRUE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = DTRAIN_RANDOM_REGRESSION
, nfold = nfolds
......@@ -2286,11 +2389,12 @@ test_that("lgb.cv() respects changes to logging verbosity", {
dtrain <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
# (verbose = 1) should be INFO and WARNING level logs
lgb_cv_logs <- capture.output({
cv_bst <- lgb.cv(
params = list()
params = list(num_threads = .LGB_MAX_THREADS)
, nfold = 2L
, nrounds = 5L
, data = dtrain
......@@ -2304,7 +2408,7 @@ test_that("lgb.cv() respects changes to logging verbosity", {
# (verbose = 0) should be WARNING level logs only
lgb_cv_logs <- capture.output({
cv_bst <- lgb.cv(
params = list()
params = list(num_threads = .LGB_MAX_THREADS)
, nfold = 2L
, nrounds = 5L
, data = dtrain
......@@ -2318,7 +2422,7 @@ test_that("lgb.cv() respects changes to logging verbosity", {
# (verbose = -1) no logs
lgb_cv_logs <- capture.output({
cv_bst <- lgb.cv(
params = list()
params = list(num_threads = .LGB_MAX_THREADS)
, nfold = 2L
, nrounds = 5L
, data = dtrain
......@@ -2336,6 +2440,7 @@ test_that("lgb.cv() updates params based on keyword arguments", {
dtrain <- lgb.Dataset(
data = matrix(rnorm(400L), ncol = 4L)
, label = rnorm(100L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
# defaults from keyword arguments should be used if not specified in params
......@@ -2344,7 +2449,7 @@ test_that("lgb.cv() updates params based on keyword arguments", {
cv_bst <- lgb.cv(
data = dtrain
, obj = "regression"
, params = list()
, params = list(num_threads = .LGB_MAX_THREADS)
, nfold = 2L
)
})
......@@ -2365,6 +2470,7 @@ test_that("lgb.cv() updates params based on keyword arguments", {
, params = list(
"verbosity" = 5L
, "num_iterations" = 2L
, num_threads = .LGB_MAX_THREADS
)
, nfold = 2L
)
......@@ -2385,6 +2491,7 @@ test_that("lgb.cv() updates params based on keyword arguments", {
, params = list(
"verbose" = 5L
, "num_boost_round" = 2L
, num_threads = .LGB_MAX_THREADS
)
, nfold = 2L
)
......@@ -2407,6 +2514,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
return(lgb.Dataset(
data = X
, label = 2L * X + runif(nrow(X), 0L, 0.1)
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -2416,6 +2524,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -2450,11 +2559,13 @@ test_that("lgb.train() with linear learner fails already-constructed dataset wit
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- lgb.Dataset(
data = matrix(rnorm(100L), ncol = 1L)
, label = rnorm(100L)
, params = list(num_threads = .LGB_MAX_THREADS)
)
dtrain$construct()
expect_error({
......@@ -2480,6 +2591,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
return(lgb.Dataset(
data = X
, label = 2L * X + runif(nrow(X), 0L, 0.1)
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -2489,6 +2601,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -2526,6 +2639,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
return(lgb.Dataset(
data = X
, label = 2L * X + runif(nrow(X), 0L, 0.1)
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -2537,6 +2651,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
, num_leaves = 2L
, bagging_freq = 1L
, subsample = 0.8
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -2576,6 +2691,7 @@ test_that("lgb.train() works with linear learners and data where a feature has o
, label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1)
, params = list(
feature_pre_filter = FALSE
, num_threads = .LGB_MAX_THREADS
)
))
}
......@@ -2586,6 +2702,7 @@ test_that("lgb.train() works with linear learners and data where a feature has o
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -2606,6 +2723,7 @@ test_that("lgb.train() works with linear learners when Dataset has categorical f
return(lgb.Dataset(
data = X
, label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1)
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -2616,6 +2734,7 @@ test_that("lgb.train() works with linear learners when Dataset has categorical f
, seed = 0L
, num_leaves = 2L
, categorical_feature = 1L
, num_threads = .LGB_MAX_THREADS
)
dtrain <- .new_dataset()
......@@ -2682,12 +2801,13 @@ test_that("lgb.train() throws an informative error if interaction_constraints co
test_that(paste0("lgb.train() gives same result when interaction_constraints is specified as a list of ",
"character vectors, numeric vectors, or a combination"), {
set.seed(1L)
dtrain <- lgb.Dataset(train$data, label = train$label)
dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))
params <- list(
objective = "regression"
, interaction_constraints = list(c(1L, 2L), 3L)
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = dtrain
......@@ -2701,6 +2821,7 @@ test_that(paste0("lgb.train() gives same result when interaction_constraints is
objective = "regression"
, interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), cnames[[3L]])
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = dtrain
......@@ -2713,6 +2834,7 @@ test_that(paste0("lgb.train() gives same result when interaction_constraints is
objective = "regression"
, interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), 3L)
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = dtrain
......@@ -2728,12 +2850,13 @@ test_that(paste0("lgb.train() gives same result when interaction_constraints is
test_that(paste0("lgb.train() gives same results when using interaction_constraints and specifying colnames"), {
set.seed(1L)
dtrain <- lgb.Dataset(train$data, label = train$label)
dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))
params <- list(
objective = "regression"
, interaction_constraints = list(c(1L, 2L), 3L)
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = dtrain
......@@ -2747,6 +2870,7 @@ test_that(paste0("lgb.train() gives same results when using interaction_constrai
objective = "regression"
, interaction_constraints = list(c(new_colnames[1L], new_colnames[2L]), new_colnames[3L])
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = dtrain
......@@ -2796,6 +2920,7 @@ test_that(paste0("lgb.train() gives same results when using interaction_constrai
, categorical_feature = categorical_features
, free_raw_data = FALSE
, colnames = c("feature_1", "feature_2", "feature_3")
, params = list(num_threads = .LGB_MAX_THREADS)
))
}
......@@ -2891,6 +3016,7 @@ for (x3_to_categorical in c(TRUE, FALSE)) {
, monotone_constraints_method = monotone_constraints_method
, use_missing = FALSE
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
constrained_model <- lgb.train(
params = params
......@@ -2912,7 +3038,7 @@ test_that("lightgbm() accepts objective as function argument and under params",
bst1 <- lightgbm(
data = train$data
, label = train$label
, params = list(objective = "regression_l1")
, params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS)
, nrounds = 5L
, verbose = VERBOSITY
)
......@@ -2947,7 +3073,7 @@ test_that("lightgbm() prioritizes objective under params over objective as funct
data = train$data
, label = train$label
, objective = "regression"
, params = list(objective = "regression_l1")
, params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS)
, nrounds = 5L
, verbose = VERBOSITY
)
......@@ -2964,7 +3090,7 @@ test_that("lightgbm() prioritizes objective under params over objective as funct
data = train$data
, label = train$label
, objective = "regression"
, params = list(loss = "regression_l1")
, params = list(loss = "regression_l1", num_threads = .LGB_MAX_THREADS)
, nrounds = 5L
, verbose = VERBOSITY
)
......@@ -2985,6 +3111,7 @@ test_that("lightgbm() accepts init_score as function argument", {
, objective = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred1 <- predict(bst1, train$data, type = "raw")
......@@ -2995,6 +3122,7 @@ test_that("lightgbm() accepts init_score as function argument", {
, objective = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred2 <- predict(bst2, train$data, type = "raw")
......@@ -3007,6 +3135,7 @@ test_that("lightgbm() defaults to 'regression' objective if objective not otherw
, label = train$label
, nrounds = 5L
, verbose = VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
expect_equal(bst$params$objective, "regression")
model_txt_lines <- strsplit(
......@@ -3081,6 +3210,7 @@ test_that("lightgbm() accepts 'weight' and 'weights'", {
, params = list(
min_data_in_bin = 1L
, min_data_in_leaf = 1L
, num_threads = .LGB_MAX_THREADS
)
)
expect_equal(model$.__enclos_env__$private$train_set$get_field("weight"), w)
......@@ -3146,6 +3276,7 @@ test_that("lightgbm() accepts 'weight' and 'weights'", {
, objective = "binary"
, metric = "auc"
, early_stopping_round = nrounds
, num_threads = .LGB_MAX_THREADS
)
if (!is.null(verbose_param)) {
params[["verbose"]] <- verbose_param
......@@ -3162,6 +3293,7 @@ test_that("lightgbm() accepts 'weight' and 'weights'", {
train_kwargs[["data"]] <- lgb.Dataset(
data = train$data
, label = train$label
, params = list(num_threads = .LGB_MAX_THREADS)
)
train_kwargs[["valids"]] <- list(
"valid" = lgb.Dataset(data = test$data, label = test$label)
......@@ -3535,7 +3667,7 @@ test_that("lightgbm() changes objective='auto' appropriately", {
data("mtcars")
y <- mtcars$mpg
x <- as.matrix(mtcars[, -1L])
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
expect_equal(model$params$objective, "regression")
model_txt_lines <- strsplit(
x = model$save_model_to_string()
......@@ -3548,7 +3680,7 @@ test_that("lightgbm() changes objective='auto' appropriately", {
# Binary classification
x <- train$data
y <- factor(train$label)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
expect_equal(model$params$objective, "binary")
model_txt_lines <- strsplit(
x = model$save_model_to_string()
......@@ -3561,7 +3693,7 @@ test_that("lightgbm() changes objective='auto' appropriately", {
data("iris")
y <- factor(iris$Species)
x <- as.matrix(iris[, -5L])
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
expect_equal(model$params$objective, "multiclass")
expect_equal(model$params$num_class, 3L)
model_txt_lines <- strsplit(
......@@ -3576,7 +3708,14 @@ test_that("lightgbm() determines number of classes for non-default multiclass ob
data("iris")
y <- factor(iris$Species)
x <- as.matrix(iris[, -5L])
model <- lightgbm(x, y, objective = "multiclassova", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(
x
, y
, objective = "multiclassova"
, verbose = VERBOSITY
, nrounds = 5L
, num_threads = .LGB_MAX_THREADS
)
expect_equal(model$params$objective, "multiclassova")
expect_equal(model$params$num_class, 3L)
model_txt_lines <- strsplit(
......@@ -3592,7 +3731,7 @@ test_that("lightgbm() doesn't accept binary classification with non-binary facto
y <- factor(iris$Species)
x <- as.matrix(iris[, -5L])
expect_error({
lightgbm(x, y, objective = "binary", verbose = VERBOSITY, nrounds = 5L)
lightgbm(x, y, objective = "binary", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
}, regexp = "Factors with >2 levels as labels only allowed for multi-class objectives")
})
......@@ -3603,7 +3742,7 @@ test_that("lightgbm() doesn't accept multi-class classification with binary fact
y <- factor(y)
x <- as.matrix(iris[, -5L])
expect_error({
lightgbm(x, y, objective = "multiclass", verbose = VERBOSITY, nrounds = 5L)
lightgbm(x, y, objective = "multiclass", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
}, regexp = "Two-level factors as labels only allowed for objective='binary'")
})
......@@ -3611,7 +3750,7 @@ test_that("lightgbm() model predictions retain factor levels for multiclass clas
data("iris")
y <- factor(iris$Species)
x <- as.matrix(iris[, -5L])
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
pred <- predict(model, x, type = "class")
expect_true(is.factor(pred))
......@@ -3630,7 +3769,7 @@ test_that("lightgbm() model predictions retain factor levels for binary classifi
y[y == "setosa"] <- "versicolor"
y <- factor(y)
x <- as.matrix(iris[, -5L])
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L)
model <- lightgbm(x, y, objective = "auto", verbose = VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)
pred <- predict(model, x, type = "class")
expect_true(is.factor(pred))
......
R-package/tests/testthat/test_custom_objective.R
View file @ 7dcbb8cd
......@@ -39,6 +39,7 @@ param <- list(
, objective = logregobj
, metric = "auc"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
num_round <- 10L
......@@ -54,6 +55,7 @@ test_that("using a custom objective, custom eval, and no other metrics works", {
num_leaves = 8L
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = 4L
......
R-package/tests/testthat/test_dataset.R
View file @ 7dcbb8cd
......@@ -133,7 +133,7 @@ test_that("Dataset$set_reference() updates categorical_feature, colnames, and pr
dtrain$construct()
bst <- Booster$new(
train_set = dtrain
, params = list(verbose = -1L)
, params = list(verbose = -1L, num_threads = .LGB_MAX_THREADS)
)
dtrain$.__enclos_env__$private$predictor <- bst$to_predictor()
......@@ -394,6 +394,7 @@ test_that("lgb.Dataset: should be able to run lgb.train() immediately after usin
, num_leaves = 5L
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
# should be able to train right away
......@@ -429,6 +430,7 @@ test_that("lgb.Dataset: should be able to run lgb.cv() immediately after using l
, learning_rate = 1.0
, num_iterations = 5L
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
# should be able to train right away
......
R-package/tests/testthat/test_learning_to_rank.R
View file @ 7dcbb8cd
......@@ -27,6 +27,7 @@ test_that("learning-to-rank with lgb.train() works as expected", {
, lambdarank_truncation_level = 3L
, learning_rate = 0.001
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(
params = params
......@@ -91,6 +92,7 @@ test_that("learning-to-rank with lgb.cv() works as expected", {
, min_data = 1L
, learning_rate = 0.01
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
nfold <- 4L
nrounds <- 10L
......
R-package/tests/testthat/test_lgb.Booster.R
View file @ 7dcbb8cd
......@@ -13,6 +13,7 @@ test_that("Booster$finalize() should not fail", {
data = dtrain
, params = list(
objective = "regression"
, num_threads = .LGB_MAX_THREADS
)
, verbose = VERBOSITY
, nrounds = 3L
......@@ -66,6 +67,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
, min_data = 1L
, learning_rate = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = 5L
......@@ -185,6 +187,7 @@ test_that("Loading a Booster from a text file works", {
, learning_rate = 1.0
, objective = "binary"
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- lightgbm(
data = as.matrix(train$data)
......@@ -227,6 +230,7 @@ test_that("boosters with linear models at leaves can be written to text file and
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
bst <- lgb.train(
......@@ -268,6 +272,7 @@ test_that("Loading a Booster from a string works", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 2L
)
......@@ -299,6 +304,7 @@ test_that("Saving a large model to string should work", {
num_leaves = 100L
, learning_rate = 0.01
, objective = "binary"
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 500L
, verbose = VERBOSITY
......@@ -342,6 +348,7 @@ test_that("Saving a large model to JSON should work", {
num_leaves = 100L
, learning_rate = 0.01
, objective = "binary"
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 200L
, verbose = VERBOSITY
......@@ -372,6 +379,7 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 2L
)
......@@ -406,6 +414,7 @@ test_that("Creating a Booster from a Dataset should work", {
params = list(
objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
),
train_set = dtrain
)
......@@ -427,6 +436,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -440,6 +450,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
train_set = dtest
, params = list(
verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
)
expect_true(lgb.is.Booster(bst))
......@@ -463,6 +474,7 @@ test_that("Booster$eval() should work on a Dataset stored in a binary file", {
, metric = "l2"
, num_leaves = 4L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = 2L
......@@ -492,7 +504,7 @@ test_that("Booster$eval() should work on a Dataset stored in a binary file", {
eval_from_file <- bst$eval(
data = lgb.Dataset(
data = test_file
, params = list(verbose = VERBOSITY)
, params = list(verbose = VERBOSITY, num_threads = .LGB_MAX_THREADS)
)$construct()
, name = "test"
)
......@@ -521,6 +533,7 @@ test_that("Booster$rollback_one_iter() should work as expected", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -555,6 +568,7 @@ test_that("Booster$update() passing a train_set works as expected", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -579,6 +593,7 @@ test_that("Booster$update() passing a train_set works as expected", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds + 1L
)
......@@ -604,6 +619,7 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = nrounds
)
......@@ -631,6 +647,7 @@ test_that("Booster should store parameters and Booster$reset_parameter() should
, boosting = "gbdt"
, num_class = 5L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- Booster$new(
params = params
......@@ -658,6 +675,7 @@ test_that("Booster$params should include dataset params, before and after Booste
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- Booster$new(
params = params
......@@ -670,6 +688,7 @@ test_that("Booster$params should include dataset params, before and after Booste
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
, max_bin = 17L
)
)
......@@ -681,6 +700,7 @@ test_that("Booster$params should include dataset params, before and after Booste
, max_depth = 4L
, bagging_fraction = 0.9
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
, max_bin = 17L
)
expect_identical(ret_bst$params, expected_params)
......@@ -699,6 +719,7 @@ test_that("Saving a model with different feature importance types works", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 2L
)
......@@ -754,6 +775,7 @@ test_that("Saving a model with unknown importance type fails", {
, learning_rate = 1.0
, objective = "binary"
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 2L
)
......@@ -789,6 +811,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
params = list(
objective = "regression"
, metric = "l2"
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, nrounds = nrounds
......@@ -845,6 +868,7 @@ test_that("early_stopping, num_iterations are stored correctly in model string e
, n_iter = n_iter
, early_stopping_round = early_stopping_round
, n_iter_no_change = n_iter_no_change
, num_threads = .LGB_MAX_THREADS
)
bst <- lgb.train(
......@@ -884,6 +908,7 @@ test_that("Booster: method calls Booster with a null handle should raise an info
objective = "regression"
, metric = "l2"
, num_leaves = 8L
, num_threads = .LGB_MAX_THREADS
)
, data = dtrain
, verbose = VERBOSITY
......@@ -1074,6 +1099,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
, early_stopping_round = early_stopping_round
, n_iter_no_change = n_iter_no_change
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
cv_bst <- lgb.cv(
......@@ -1118,6 +1144,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- Booster$new(
params = params
......@@ -1134,6 +1161,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
, max_bin = 17L
)
)
......@@ -1153,6 +1181,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
bst <- Booster$new(
params = params
......@@ -1169,6 +1198,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
, max_depth = 4L
, bagging_fraction = 0.8
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
, max_bin = 17L
)
)
......@@ -1184,6 +1214,7 @@ test_that("Handle is automatically restored when calling predict", {
, params = list(
verbose = VERBOSITY
)
, num_threads = .LGB_MAX_THREADS
)
bst_file <- tempfile(fileext = ".rds")
saveRDS(bst, file = bst_file)
......@@ -1209,6 +1240,7 @@ test_that("boosters with linear models at leaves work with saveRDS.lgb.Booster a
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
bst <- lgb.train(
......@@ -1248,6 +1280,7 @@ test_that("boosters with linear models at leaves can be written to RDS and re-lo
, metric = "mse"
, seed = 0L
, num_leaves = 2L
, num_threads = .LGB_MAX_THREADS
)
bst <- lgb.train(
......@@ -1344,6 +1377,7 @@ test_that("Booster's print, show, and summary work correctly", {
params = list(
objective = "regression"
, min_data_in_leaf = 1L
, num_threads = .LGB_MAX_THREADS
)
, data = lgb.Dataset(
as.matrix(mtcars[, -1L])
......@@ -1359,7 +1393,7 @@ test_that("Booster's print, show, and summary work correctly", {
data("iris")
model <- lgb.train(
params = list(objective = "multiclass", num_class = 3L)
params = list(objective = "multiclass", num_class = 3L, num_threads = .LGB_MAX_THREADS)
, data = lgb.Dataset(
as.matrix(iris[, -5L])
, label = as.numeric(factor(iris$Species)) - 1.0
......@@ -1399,6 +1433,7 @@ test_that("Booster's print, show, and summary work correctly", {
, eval = .evalerror
, verbose = VERBOSITY
, nrounds = 5L
, params = list(num_threads = .LGB_MAX_THREADS)
)
.check_methods_work(model)
......@@ -1410,6 +1445,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
params = list(
objective = "regression"
, min_data_in_leaf = 1L
, num_threads = .LGB_MAX_THREADS
)
, data = lgb.Dataset(
as.matrix(mtcars[, -1L])
......
R-package/tests/testthat/test_lgb.interprete.R
View file @ 7dcbb8cd
......@@ -31,6 +31,7 @@ test_that("lgb.intereprete works as expected for binary classification", {
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(
params = params
......@@ -83,6 +84,7 @@ test_that("lgb.intereprete works as expected for multiclass classification", {
, learning_rate = 0.00001
, min_data = 1L
, verbose = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(
params = params
......
R-package/tests/testthat/test_lgb.plot.importance.R
View file @ 7dcbb8cd
......@@ -14,6 +14,7 @@ test_that("lgb.plot.importance() should run without error for well-formed inputs
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(params, dtrain, 3L)
tree_imp <- lgb.importance(model, percentage = TRUE)
......
R-package/tests/testthat/test_lgb.plot.interpretation.R
View file @ 7dcbb8cd
......@@ -31,6 +31,7 @@ test_that("lgb.plot.interepretation works as expected for binary classification"
, min_data_in_leaf = 1L
, min_sum_hessian_in_leaf = 1.0
, verbosity = VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(
params = params
......@@ -80,6 +81,7 @@ test_that("lgb.plot.interepretation works as expected for multiclass classificat
, num_class = 3L
, learning_rate = 0.00001
, min_data = 1L
, num_threads = .LGB_MAX_THREADS
)
model <- lgb.train(
params = params
......
R-package/tests/testthat/test_parameters.R
View file @ 7dcbb8cd
......@@ -20,6 +20,7 @@ test_that("Feature penalties work properly", {
, objective = "binary"
, feature_penalty = paste0(feature_penalties, collapse = ",")
, metric = "binary_error"
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 5L
, verbose = -1L
......@@ -97,6 +98,7 @@ test_that("training should warn if you use 'dart' boosting, specified with 'boos
, learning_rate = 0.05
, objective = "binary"
, metric = "binary_error"
, num_threads = .LGB_MAX_THREADS
)
params[[boosting_param]] <- "dart"
expect_warning({
......
R-package/tests/testthat/test_weighted_loss.R
View file @ 7dcbb8cd
......@@ -9,7 +9,7 @@ test_that("Gamma regression reacts on 'weight'", {
y <- X[, 1L] + X[, 2L] + runif(n)
X_pred <- X[1L:5L, ]
params <- list(objective = "gamma")
params <- list(objective = "gamma", num_threads = .LGB_MAX_THREADS)
# Unweighted
dtrain <- lgb.Dataset(X, label = y)
......
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