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Unverified Commit 7b10baff authored by James Lamb's avatar James Lamb Committed by GitHub
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[R-package] reduce verbosity in tests using lgb.train() (#4896) 

* [R-package] reduce verbosity in tests using lgb.train()

* Update R-package/tests/testthat/test_lgb.Booster.R
parent 8a34b1af
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Showing with 139 additions and 26 deletions
R-package/tests/testthat/test_basic.R
View file @ 7b10baff
VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
context("lightgbm()") context("lightgbm()")
ON_WINDOWS <- .Platform$OS.type == "windows" ON_WINDOWS <- .Platform$OS.type == "windows"
...@@ -337,6 +341,7 @@ test_that("training continuation works", { ...@@ -337,6 +341,7 @@ test_that("training continuation works", {
, metric = "binary_logloss" , metric = "binary_logloss"
, num_leaves = 5L , num_leaves = 5L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
# train for 10 consecutive iterations # train for 10 consecutive iterations
...@@ -538,6 +543,7 @@ test_that("lgb.train() works as expected with multiple eval metrics", { ...@@ -538,6 +543,7 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
objective = "binary" objective = "binary"
, metric = metrics , metric = metrics
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
, valids = list( , valids = list(
"train" = lgb.Dataset( "train" = lgb.Dataset(
...@@ -557,7 +563,11 @@ test_that("lgb.train() works as expected with multiple eval metrics", { ...@@ -557,7 +563,11 @@ test_that("lgb.train() works as expected with multiple eval metrics", {
test_that("lgb.train() rejects negative or 0 value passed to nrounds", { 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(objective = "regression", metric = "l2,l1") params <- list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
)
for (nround_value in c(-10L, 0L)) { for (nround_value in c(-10L, 0L)) {
expect_error({ expect_error({
bst <- lgb.train( bst <- lgb.train(
...@@ -585,6 +595,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet ...@@ -585,6 +595,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
, metric = "l2" , metric = "l2"
, num_leaves = 5L , num_leaves = 5L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
, verbose = VERBOSITY
) )
) )
...@@ -600,6 +611,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet ...@@ -600,6 +611,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
, num_leaves = 5L , num_leaves = 5L
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
, verbose = VERBOSITY
) )
) )
...@@ -616,6 +628,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet ...@@ -616,6 +628,7 @@ test_that("lgb.train() accepts nrounds as either a top-level argument or paramet
, num_leaves = 5L , num_leaves = 5L
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
, verbose = VERBOSITY
) )
) )
...@@ -651,7 +664,11 @@ test_that("lgb.train() throws an informative error if 'data' is not an lgb.Datas ...@@ -651,7 +664,11 @@ test_that("lgb.train() throws an informative error if 'data' is not an lgb.Datas
for (val in bad_values) { for (val in bad_values) {
expect_error({ expect_error({
bst <- lgb.train( bst <- lgb.train(
params = list(objective = "regression", metric = "l2,l1") params = list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
)
, data = val , data = val
, 10L , 10L
) )
...@@ -666,7 +683,11 @@ test_that("lgb.train() throws an informative error if 'valids' is not a list of ...@@ -666,7 +683,11 @@ test_that("lgb.train() throws an informative error if 'valids' is not a list of
) )
expect_error({ expect_error({
bst <- lgb.train( bst <- lgb.train(
params = list(objective = "regression", metric = "l2,l1") params = list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
)
, data = lgb.Dataset(train$data, label = train$label) , data = lgb.Dataset(train$data, label = train$label)
, 10L , 10L
, valids = valids , valids = valids
...@@ -681,7 +702,11 @@ test_that("lgb.train() errors if 'valids' is a list of lgb.Dataset objects but s ...@@ -681,7 +702,11 @@ test_that("lgb.train() errors if 'valids' is a list of lgb.Dataset objects but s
) )
expect_error({ expect_error({
bst <- lgb.train( bst <- lgb.train(
params = list(objective = "regression", metric = "l2,l1") params = list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
)
, data = lgb.Dataset(train$data, label = train$label) , data = lgb.Dataset(train$data, label = train$label)
, 10L , 10L
, valids = valids , valids = valids
...@@ -696,7 +721,11 @@ test_that("lgb.train() throws an informative error if 'valids' contains lgb.Data ...@@ -696,7 +721,11 @@ test_that("lgb.train() throws an informative error if 'valids' contains lgb.Data
) )
expect_error({ expect_error({
bst <- lgb.train( bst <- lgb.train(
params = list(objective = "regression", metric = "l2,l1") params = list(
objective = "regression"
, metric = "l2,l1"
, verbose = VERBOSITY
)
, data = lgb.Dataset(train$data, label = train$label) , data = lgb.Dataset(train$data, label = train$label)
, 10L , 10L
, valids = valids , valids = valids
...@@ -715,6 +744,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", { ...@@ -715,6 +744,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, force_col_wise = TRUE , force_col_wise = TRUE
, verbose = VERBOSITY
) )
bst_col_wise <- lgb.train( bst_col_wise <- lgb.train(
params = params params = params
...@@ -726,6 +756,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", { ...@@ -726,6 +756,7 @@ test_that("lgb.train() works with force_col_wise and force_row_wise", {
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, force_row_wise = TRUE , force_row_wise = TRUE
, verbose = VERBOSITY
) )
bst_row_wise <- lgb.train( bst_row_wise <- lgb.train(
params = params params = params
...@@ -764,6 +795,7 @@ test_that("lgb.train() works as expected with sparse features", { ...@@ -764,6 +795,7 @@ test_that("lgb.train() works as expected with sparse features", {
objective = "binary" objective = "binary"
, min_data = 1L , min_data = 1L
, min_data_in_bin = 1L , min_data_in_bin = 1L
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -804,6 +836,7 @@ test_that("lgb.train() works with early stopping for classification", { ...@@ -804,6 +836,7 @@ test_that("lgb.train() works with early stopping for classification", {
params = list( params = list(
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -827,6 +860,7 @@ test_that("lgb.train() works with early stopping for classification", { ...@@ -827,6 +860,7 @@ test_that("lgb.train() works with early stopping for classification", {
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -875,6 +909,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi ...@@ -875,6 +909,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
params = list( params = list(
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -898,6 +933,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi ...@@ -898,6 +933,7 @@ test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stoppi
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, n_iter_no_change = value , n_iter_no_change = value
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -937,6 +973,7 @@ test_that("lgb.train() works with early stopping for classification with a metri ...@@ -937,6 +973,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
, metric = "auc" , metric = "auc"
, max_depth = 3L , max_depth = 3L
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -950,6 +987,7 @@ test_that("lgb.train() works with early stopping for classification with a metri ...@@ -950,6 +987,7 @@ test_that("lgb.train() works with early stopping for classification with a metri
, metric = "binary_error" , metric = "binary_error"
, max_depth = 3L , max_depth = 3L
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -1008,6 +1046,7 @@ test_that("lgb.train() works with early stopping for regression", { ...@@ -1008,6 +1046,7 @@ test_that("lgb.train() works with early stopping for regression", {
params = list( params = list(
objective = "regression" objective = "regression"
, metric = "rmse" , metric = "rmse"
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -1031,6 +1070,7 @@ test_that("lgb.train() works with early stopping for regression", { ...@@ -1031,6 +1070,7 @@ test_that("lgb.train() works with early stopping for regression", {
objective = "regression" objective = "regression"
, metric = "rmse" , metric = "rmse"
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -1065,6 +1105,7 @@ test_that("lgb.train() does not stop early if early_stopping_rounds is not given ...@@ -1065,6 +1105,7 @@ test_that("lgb.train() does not stop early if early_stopping_rounds is not given
params = list( params = list(
objective = "regression" objective = "regression"
, metric = "None" , metric = "None"
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_REGRESSION , data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds , nrounds = nrounds
...@@ -1108,12 +1149,14 @@ test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to ...@@ -1108,12 +1149,14 @@ test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to
objective = "regression" objective = "regression"
, metric = "None" , metric = "None"
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, list( , list(
objective = "regression" objective = "regression"
, metric = "None" , metric = "None"
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, first_metric_only = FALSE , first_metric_only = FALSE
, verbose = VERBOSITY
) )
) )
...@@ -1176,6 +1219,7 @@ test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based ...@@ -1176,6 +1219,7 @@ test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based
, metric = "None" , metric = "None"
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, first_metric_only = TRUE , first_metric_only = TRUE
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_REGRESSION , data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds , nrounds = nrounds
...@@ -1221,6 +1265,7 @@ test_that("lgb.train() works when a mixture of functions and strings are passed ...@@ -1221,6 +1265,7 @@ test_that("lgb.train() works when a mixture of functions and strings are passed
params = list( params = list(
objective = "regression" objective = "regression"
, metric = "None" , metric = "None"
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_REGRESSION , data = DTRAIN_RANDOM_REGRESSION
, nrounds = nrounds , nrounds = nrounds
...@@ -1276,6 +1321,7 @@ test_that("lgb.train() works when a list of strings or a character vector is pas ...@@ -1276,6 +1321,7 @@ test_that("lgb.train() works when a list of strings or a character vector is pas
params = list( params = list(
objective = "binary" objective = "binary"
, metric = "None" , metric = "None"
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_CLASSIFICATION , data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds , nrounds = nrounds
...@@ -1312,6 +1358,7 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri ...@@ -1312,6 +1358,7 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri
params = list( params = list(
objective = "binary" objective = "binary"
, metric = "binary_error" , metric = "binary_error"
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_CLASSIFICATION , data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds , nrounds = nrounds
...@@ -1343,6 +1390,7 @@ test_that("lgb.train() works when you give a function for eval", { ...@@ -1343,6 +1390,7 @@ test_that("lgb.train() works when you give a function for eval", {
params = list( params = list(
objective = "binary" objective = "binary"
, metric = "None" , metric = "None"
, verbose = VERBOSITY
) )
, data = DTRAIN_RANDOM_CLASSIFICATION , data = DTRAIN_RANDOM_CLASSIFICATION
, nrounds = nrounds , nrounds = nrounds
...@@ -1391,6 +1439,7 @@ test_that("lgb.train() works with early stopping for regression with a metric th ...@@ -1391,6 +1439,7 @@ test_that("lgb.train() works with early stopping for regression with a metric th
) )
, min_data_in_bin = 5L , min_data_in_bin = 5L
, early_stopping_rounds = early_stopping_rounds , early_stopping_rounds = early_stopping_rounds
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
...@@ -1430,6 +1479,7 @@ test_that("lgb.train() supports non-ASCII feature names", { ...@@ -1430,6 +1479,7 @@ test_that("lgb.train() supports non-ASCII feature names", {
, obj = "regression" , obj = "regression"
, params = list( , params = list(
metric = "rmse" metric = "rmse"
, verbose = VERBOSITY
) )
, colnames = feature_names , colnames = feature_names
) )
...@@ -1512,6 +1562,7 @@ test_that("when early stopping is not activated, best_iter and best_score come f ...@@ -1512,6 +1562,7 @@ test_that("when early stopping is not activated, best_iter and best_score come f
, metric = "rmse" , metric = "rmse"
, learning_rate = 1.5 , learning_rate = 1.5
, num_leaves = 5L , num_leaves = 5L
, verbose = VERBOSITY
) )
# example 1: two valids, neither are the training data # example 1: two valids, neither are the training data
...@@ -1671,6 +1722,7 @@ test_that("lightgbm.train() gives the correct best_score and best_iter for a met ...@@ -1671,6 +1722,7 @@ test_that("lightgbm.train() gives the correct best_score and best_iter for a met
, metric = "auc" , metric = "auc"
, learning_rate = 1.5 , learning_rate = 1.5
, num_leaves = 5L , num_leaves = 5L
, verbose = VERBOSITY
) )
) )
# note that "something-random-we-would-not-hardcode" was recognized as the training # note that "something-random-we-would-not-hardcode" was recognized as the training
...@@ -1915,7 +1967,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear ...@@ -1915,7 +1967,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
...@@ -1949,7 +2001,7 @@ test_that("lgb.train() w/ linear learner fails already-constructed dataset with ...@@ -1949,7 +2001,7 @@ test_that("lgb.train() w/ linear learner fails already-constructed dataset with
set.seed(708L) set.seed(708L)
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
...@@ -1986,7 +2038,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va ...@@ -1986,7 +2038,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
...@@ -2032,7 +2084,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h ...@@ -2032,7 +2084,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
......
R-package/tests/testthat/test_custom_objective.R
View file @ 7b10baff
VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
context("Test models with custom objective") context("Test models with custom objective")
data(agaricus.train, package = "lightgbm") data(agaricus.train, package = "lightgbm")
...@@ -36,6 +40,7 @@ param <- list( ...@@ -36,6 +40,7 @@ param <- list(
, learning_rate = 1.0 , learning_rate = 1.0
, objective = logregobj , objective = logregobj
, metric = "auc" , metric = "auc"
, verbose = VERBOSITY
) )
num_round <- 10L num_round <- 10L
...@@ -50,6 +55,7 @@ test_that("using a custom objective, custom eval, and no other metrics works", { ...@@ -50,6 +55,7 @@ test_that("using a custom objective, custom eval, and no other metrics works", {
params = list( params = list(
num_leaves = 8L num_leaves = 8L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = 4L , nrounds = 4L
......
R-package/tests/testthat/test_dataset.R
View file @ 7b10baff
VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
context("testing lgb.Dataset functionality") context("testing lgb.Dataset functionality")
data(agaricus.train, package = "lightgbm") data(agaricus.train, package = "lightgbm")
...@@ -368,6 +372,7 @@ test_that("lgb.Dataset: should be able to run lgb.train() immediately after usin ...@@ -368,6 +372,7 @@ test_that("lgb.Dataset: should be able to run lgb.train() immediately after usin
, metric = "binary_logloss" , metric = "binary_logloss"
, num_leaves = 5L , num_leaves = 5L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
# should be able to train right away # should be able to train right away
......
R-package/tests/testthat/test_learning_to_rank.R
View file @ 7b10baff
VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
context("Learning to rank") context("Learning to rank")
# numerical tolerance to use when checking metric values # numerical tolerance to use when checking metric values
...@@ -25,6 +29,7 @@ test_that("learning-to-rank with lgb.train() works as expected", { ...@@ -25,6 +29,7 @@ test_that("learning-to-rank with lgb.train() works as expected", {
, ndcg_at = ndcg_at , ndcg_at = ndcg_at
, lambdarank_truncation_level = 3L , lambdarank_truncation_level = 3L
, learning_rate = 0.001 , learning_rate = 0.001
, verbose = VERBOSITY
) )
model <- lgb.train( model <- lgb.train(
params = params params = params
......
R-package/tests/testthat/test_lgb.Booster.R
View file @ 7b10baff
VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
context("Booster") context("Booster")
ON_WINDOWS <- .Platform$OS.type == "windows" ON_WINDOWS <- .Platform$OS.type == "windows"
...@@ -12,7 +16,7 @@ test_that("Booster$finalize() should not fail", { ...@@ -12,7 +16,7 @@ test_that("Booster$finalize() should not fail", {
, params = list( , params = list(
objective = "regression" objective = "regression"
) )
, verbose = -1L , verbose = VERBOSITY
, nrounds = 3L , nrounds = 3L
) )
expect_true(lgb.is.Booster(bst)) expect_true(lgb.is.Booster(bst))
...@@ -65,6 +69,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect ...@@ -65,6 +69,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
, metric = "l2" , metric = "l2"
, min_data = 1L , min_data = 1L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = 5L , nrounds = 5L
...@@ -98,6 +103,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect ...@@ -98,6 +103,7 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
, metric = "l2" , metric = "l2"
, min_data = 1L , min_data = 1L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = 5L , nrounds = 5L
...@@ -133,6 +139,7 @@ test_that("lgb.load() gives the expected error messages given different incorrec ...@@ -133,6 +139,7 @@ test_that("lgb.load() gives the expected error messages given different incorrec
objective = "binary" objective = "binary"
, num_leaves = 4L , num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -179,6 +186,7 @@ test_that("Loading a Booster from a text file works", { ...@@ -179,6 +186,7 @@ test_that("Loading a Booster from a text file works", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -221,6 +229,7 @@ test_that("boosters with linear models at leaves can be written to text file and ...@@ -221,6 +229,7 @@ test_that("boosters with linear models at leaves can be written to text file and
data = dtrain data = dtrain
, nrounds = 10L , nrounds = 10L
, params = params , params = params
, verbose = VERBOSITY
) )
expect_true(lgb.is.Booster(bst)) expect_true(lgb.is.Booster(bst))
...@@ -254,6 +263,7 @@ test_that("Loading a Booster from a string works", { ...@@ -254,6 +263,7 @@ test_that("Loading a Booster from a string works", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -289,7 +299,7 @@ test_that("Saving a large model to string should work", { ...@@ -289,7 +299,7 @@ test_that("Saving a large model to string should work", {
) )
, nrounds = 500L , nrounds = 500L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
, verbose = -1L , verbose = VERBOSITY
) )
pred <- predict(bst, train$data) pred <- predict(bst, train$data)
...@@ -333,7 +343,7 @@ test_that("Saving a large model to JSON should work", { ...@@ -333,7 +343,7 @@ test_that("Saving a large model to JSON should work", {
) )
, nrounds = 200L , nrounds = 200L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
, verbose = -1L , verbose = VERBOSITY
) )
model_json <- bst$dump_model() model_json <- bst$dump_model()
...@@ -360,6 +370,7 @@ test_that("If a string and a file are both passed to lgb.load() the file is used ...@@ -360,6 +370,7 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -396,6 +407,7 @@ test_that("Creating a Booster from a Dataset should work", { ...@@ -396,6 +407,7 @@ test_that("Creating a Booster from a Dataset should work", {
bst <- Booster$new( bst <- Booster$new(
params = list( params = list(
objective = "binary" objective = "binary"
, verbose = VERBOSITY
), ),
train_set = dtrain train_set = dtrain
) )
...@@ -416,6 +428,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w ...@@ -416,6 +428,7 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -428,6 +441,9 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w ...@@ -428,6 +441,9 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
) )
bst_from_ds <- Booster$new( bst_from_ds <- Booster$new(
train_set = dtest train_set = dtest
, params = list(
verbose = VERBOSITY
)
) )
expect_true(lgb.is.Booster(bst)) expect_true(lgb.is.Booster(bst))
expect_equal(bst$current_iter(), nrounds) expect_equal(bst$current_iter(), nrounds)
...@@ -449,6 +465,7 @@ test_that("Booster$eval() should work on a Dataset stored in a binary file", { ...@@ -449,6 +465,7 @@ test_that("Booster$eval() should work on a Dataset stored in a binary file", {
objective = "regression" objective = "regression"
, metric = "l2" , metric = "l2"
, num_leaves = 4L , num_leaves = 4L
, verbose = VERBOSITY
) )
, data = dtrain , data = dtrain
, nrounds = 2L , nrounds = 2L
...@@ -505,6 +522,7 @@ test_that("Booster$rollback_one_iter() should work as expected", { ...@@ -505,6 +522,7 @@ test_that("Booster$rollback_one_iter() should work as expected", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -539,6 +557,7 @@ test_that("Booster$update() passing a train_set works as expected", { ...@@ -539,6 +557,7 @@ test_that("Booster$update() passing a train_set works as expected", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -562,6 +581,7 @@ test_that("Booster$update() passing a train_set works as expected", { ...@@ -562,6 +581,7 @@ test_that("Booster$update() passing a train_set works as expected", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = nrounds + 1L , nrounds = nrounds + 1L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -587,6 +607,7 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat ...@@ -587,6 +607,7 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = nrounds , nrounds = nrounds
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -614,6 +635,7 @@ test_that("Booster should store parameters and Booster$reset_parameter() should ...@@ -614,6 +635,7 @@ test_that("Booster should store parameters and Booster$reset_parameter() should
, metric = c("multi_logloss", "multi_error") , metric = c("multi_logloss", "multi_error")
, boosting = "gbdt" , boosting = "gbdt"
, num_class = 5L , num_class = 5L
, verbose = VERBOSITY
) )
bst <- Booster$new( bst <- Booster$new(
params = params params = params
...@@ -640,6 +662,7 @@ test_that("Booster$params should include dataset params, before and after Booste ...@@ -640,6 +662,7 @@ test_that("Booster$params should include dataset params, before and after Booste
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
) )
bst <- Booster$new( bst <- Booster$new(
params = params params = params
...@@ -651,6 +674,7 @@ test_that("Booster$params should include dataset params, before and after Booste ...@@ -651,6 +674,7 @@ test_that("Booster$params should include dataset params, before and after Booste
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
, max_bin = 17L , max_bin = 17L
) )
) )
...@@ -661,6 +685,7 @@ test_that("Booster$params should include dataset params, before and after Booste ...@@ -661,6 +685,7 @@ test_that("Booster$params should include dataset params, before and after Booste
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.9 , bagging_fraction = 0.9
, verbose = VERBOSITY
, max_bin = 17L , max_bin = 17L
) )
expect_identical(ret_bst$params, expected_params) expect_identical(ret_bst$params, expected_params)
...@@ -680,6 +705,7 @@ test_that("Saving a model with different feature importance types works", { ...@@ -680,6 +705,7 @@ test_that("Saving a model with different feature importance types works", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -735,6 +761,7 @@ test_that("Saving a model with unknown importance type fails", { ...@@ -735,6 +761,7 @@ test_that("Saving a model with unknown importance type fails", {
num_leaves = 4L num_leaves = 4L
, learning_rate = 1.0 , learning_rate = 1.0
, objective = "binary" , objective = "binary"
, verbose = VERBOSITY
) )
, nrounds = 2L , nrounds = 2L
, save_name = tempfile(fileext = ".model") , save_name = tempfile(fileext = ".model")
...@@ -770,7 +797,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", { ...@@ -770,7 +797,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
) )
, data = dtrain , data = dtrain
, nrounds = nrounds , nrounds = nrounds
, verbose = 0L , verbose = VERBOSITY
) )
model_str <- bst$save_model_to_string() model_str <- bst$save_model_to_string()
...@@ -787,7 +814,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", { ...@@ -787,7 +814,7 @@ test_that("all parameters are stored correctly with save_model_to_string()", {
expect_equal(sum(params_in_file == "[objective: regression]"), 1L) expect_equal(sum(params_in_file == "[objective: regression]"), 1L)
expect_equal(sum(grepl(pattern = "^\\[verbosity\\:", x = params_in_file)), 1L) expect_equal(sum(grepl(pattern = "^\\[verbosity\\:", x = params_in_file)), 1L)
expect_equal(sum(params_in_file == "[verbosity: 0]"), 1L) expect_equal(sum(params_in_file == sprintf("[verbosity: %i]", VERBOSITY)), 1L)
# early stopping should be off by default # early stopping should be off by default
expect_equal(sum(grepl(pattern = "^\\[early_stopping_round\\:", x = params_in_file)), 1L) expect_equal(sum(grepl(pattern = "^\\[early_stopping_round\\:", x = params_in_file)), 1L)
...@@ -833,7 +860,7 @@ test_that("early_stopping, num_iterations are stored correctly in model string e ...@@ -833,7 +860,7 @@ test_that("early_stopping, num_iterations are stored correctly in model string e
, valids = list( , valids = list(
"random_valid" = dvalid "random_valid" = dvalid
) )
, verbose = 0L , verbose = VERBOSITY
) )
model_str <- bst$save_model_to_string() model_str <- bst$save_model_to_string()
...@@ -864,7 +891,7 @@ test_that("Booster: method calls Booster with a null handle should raise an info ...@@ -864,7 +891,7 @@ test_that("Booster: method calls Booster with a null handle should raise an info
, num_leaves = 8L , num_leaves = 8L
) )
, data = dtrain , data = dtrain
, verbose = -1L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
, valids = list( , valids = list(
train = dtrain train = dtrain
...@@ -937,7 +964,12 @@ test_that("Booster$new() using a Dataset with a null handle should raise an info ...@@ -937,7 +964,12 @@ test_that("Booster$new() using a Dataset with a null handle should raise an info
rm(dtrain) rm(dtrain)
dtrain <- readRDS(tmp_file) dtrain <- readRDS(tmp_file)
expect_error({ expect_error({
bst <- Booster$new(train_set = dtrain) bst <- Booster$new(
train_set = dtrain
, params = list(
verbose = VERBOSITY
)
)
}, regexp = "lgb.Booster: cannot create Booster handle") }, regexp = "lgb.Booster: cannot create Booster handle")
}) })
...@@ -969,6 +1001,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file", ...@@ -969,6 +1001,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
, n_iter = n_iter , n_iter = n_iter
, early_stopping_round = early_stopping_round , early_stopping_round = early_stopping_round
, n_iter_no_change = n_iter_no_change , n_iter_no_change = n_iter_no_change
, verbose = VERBOSITY
) )
cv_bst <- lgb.cv( cv_bst <- lgb.cv(
...@@ -977,7 +1010,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file", ...@@ -977,7 +1010,7 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
, nrounds = nrounds_kwarg , nrounds = nrounds_kwarg
, early_stopping_rounds = early_stopping_round_kwarg , early_stopping_rounds = early_stopping_round_kwarg
, nfold = 3L , nfold = 3L
, verbose = 0L , verbose = VERBOSITY
) )
for (bst in cv_bst$boosters) { for (bst in cv_bst$boosters) {
...@@ -1014,6 +1047,7 @@ test_that("params (including dataset params) should be stored in .rds file for B ...@@ -1014,6 +1047,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
) )
bst <- Booster$new( bst <- Booster$new(
params = params params = params
...@@ -1029,6 +1063,7 @@ test_that("params (including dataset params) should be stored in .rds file for B ...@@ -1029,6 +1063,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
, max_bin = 17L , max_bin = 17L
) )
) )
...@@ -1049,6 +1084,7 @@ test_that("params (including dataset params) should be stored in .rds file for B ...@@ -1049,6 +1084,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
) )
bst <- Booster$new( bst <- Booster$new(
params = params params = params
...@@ -1064,6 +1100,7 @@ test_that("params (including dataset params) should be stored in .rds file for B ...@@ -1064,6 +1100,7 @@ test_that("params (including dataset params) should be stored in .rds file for B
objective = "binary" objective = "binary"
, max_depth = 4L , max_depth = 4L
, bagging_fraction = 0.8 , bagging_fraction = 0.8
, verbose = VERBOSITY
, max_bin = 17L , max_bin = 17L
) )
) )
...@@ -1071,7 +1108,15 @@ test_that("params (including dataset params) should be stored in .rds file for B ...@@ -1071,7 +1108,15 @@ test_that("params (including dataset params) should be stored in .rds file for B
test_that("Handle is automatically restored when calling predict", { test_that("Handle is automatically restored when calling predict", {
data(agaricus.train, package = "lightgbm") data(agaricus.train, package = "lightgbm")
bst <- lightgbm(agaricus.train$data, agaricus.train$label, nrounds = 5L, obj = "binary") bst <- lightgbm(
agaricus.train$data
, agaricus.train$label
, nrounds = 5L
, obj = "binary"
, params = list(
verbose = VERBOSITY
)
)
bst_file <- tempfile(fileext = ".rds") bst_file <- tempfile(fileext = ".rds")
saveRDS(bst, file = bst_file) saveRDS(bst, file = bst_file)
...@@ -1092,7 +1137,7 @@ test_that("boosters with linear models at leaves work with saveRDS.lgb.Booster a ...@@ -1092,7 +1137,7 @@ test_that("boosters with linear models at leaves work with saveRDS.lgb.Booster a
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
...@@ -1129,7 +1174,7 @@ test_that("boosters with linear models at leaves can be written to RDS and re-lo ...@@ -1129,7 +1174,7 @@ test_that("boosters with linear models at leaves can be written to RDS and re-lo
params <- list( params <- list(
objective = "regression" objective = "regression"
, verbose = -1L , verbose = VERBOSITY
, metric = "mse" , metric = "mse"
, seed = 0L , seed = 0L
, num_leaves = 2L , num_leaves = 2L
...@@ -1190,7 +1235,7 @@ test_that("Booster's print, show, and summary work correctly", { ...@@ -1190,7 +1235,7 @@ test_that("Booster's print, show, and summary work correctly", {
, data = lgb.Dataset( , data = lgb.Dataset(
as.matrix(mtcars[, -1L]) as.matrix(mtcars[, -1L])
, label = mtcars$mpg) , label = mtcars$mpg)
, verbose = 0L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
) )
.check_methods_work(model) .check_methods_work(model)
...@@ -1202,7 +1247,7 @@ test_that("Booster's print, show, and summary work correctly", { ...@@ -1202,7 +1247,7 @@ test_that("Booster's print, show, and summary work correctly", {
as.matrix(iris[, -5L]) as.matrix(iris[, -5L])
, label = as.numeric(factor(iris$Species)) - 1.0 , label = as.numeric(factor(iris$Species)) - 1.0
) )
, verbose = 0L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
) )
.check_methods_work(model) .check_methods_work(model)
...@@ -1235,7 +1280,7 @@ test_that("Booster's print, show, and summary work correctly", { ...@@ -1235,7 +1280,7 @@ test_that("Booster's print, show, and summary work correctly", {
) )
, obj = .logregobj , obj = .logregobj
, eval = .evalerror , eval = .evalerror
, verbose = 0L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
) )
...@@ -1249,7 +1294,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", { ...@@ -1249,7 +1294,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
, data = lgb.Dataset( , data = lgb.Dataset(
as.matrix(mtcars[, -1L]) as.matrix(mtcars[, -1L])
, label = mtcars$mpg) , label = mtcars$mpg)
, verbose = 0L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
) )
ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)
...@@ -1262,7 +1307,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", { ...@@ -1262,7 +1307,7 @@ test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
as.matrix(iris[, -5L]) as.matrix(iris[, -5L])
, label = as.numeric(factor(iris$Species)) - 1.0 , label = as.numeric(factor(iris$Species)) - 1.0
) )
, verbose = 0L , verbose = VERBOSITY
, nrounds = 5L , nrounds = 5L
) )
ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)
......
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