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[R-package] consolidate testing constants in helpers file #5992

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Jul 21, 2023
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[R-package] consolidate testing constants in helpers file #5992

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6f06499
[R-package] consolidate testing constants in helpers file
jameslamb Jul 19, 2023
050058c
one more comment
jameslamb Jul 19, 2023
46fcd89
Merge branch 'master' into r/test-helpers
jameslamb Jul 20, 2023
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19 changes: 19 additions & 0 deletions R-package/tests/testthat/helper.R
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# ref for this file:
#
# * https://r-pkgs.org/testing-design.html#testthat-helper-files
# * 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.
Expand All @@ -10,3 +11,21 @@
# the check farm is a shared resource and will typically be running many checks simultaneously.
#
.LGB_MAX_THREADS <- 2L

# by default, how much should results in tests be allowed to differ from hard-coded expected numbers?
.LGB_NUMERIC_TOLERANCE <- 1e-6

# are the tests running on Windows?
.LGB_ON_WINDOWS <- .Platform$OS.type == "windows"
.LGB_ON_32_BIT_WINDOWS <- .LGB_ON_WINDOWS && .Machine$sizeof.pointer != 8L

# are the tests running in a UTF-8 locale?
.LGB_UTF8_LOCALE <- all(endsWith(
Sys.getlocale(category = "LC_CTYPE")
, "UTF-8"
))

# control how many loud LightGBM's logger is in tests
.LGB_VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)
44 changes: 19 additions & 25 deletions R-package/tests/testthat/test_Predictor.R
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Original file line number Diff line number Diff line change
@@ -1,11 +1,5 @@
library(Matrix)

VERBOSITY <- as.integer(
Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
)

TOLERANCE <- 1e-6

test_that("Predictor$finalize() should not fail", {
X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L)
y <- iris[["Sepal.Length"]]
Expand All @@ -16,7 +10,7 @@ test_that("Predictor$finalize() should not fail", {
objective = "regression"
, num_threads = .LGB_MAX_THREADS
)
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, nrounds = 3L
)
model_file <- tempfile(fileext = ".model")
Expand Down Expand Up @@ -45,7 +39,7 @@ test_that("predictions do not fail for integer input", {
objective = "regression"
, num_threads = .LGB_MAX_THREADS
)
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, nrounds = 3L
)
X_double <- X[c(1L, 51L, 101L), , drop = FALSE]
Expand Down Expand Up @@ -78,7 +72,7 @@ test_that("start_iteration works correctly", {
num_leaves = 4L
, learning_rate = 0.6
, objective = "binary"
, verbosity = VERBOSITY
, verbosity = .LGB_VERBOSITY
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 50L
Expand Down Expand Up @@ -128,7 +122,7 @@ test_that("Feature contributions from sparse inputs produce sparse outputs", {
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)

Expand Down Expand Up @@ -159,7 +153,7 @@ test_that("Sparse feature contribution predictions do not take inputs with wrong
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)

Expand Down Expand Up @@ -189,7 +183,7 @@ test_that("Feature contribution predictions do not take non-general CSR or CSC i
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)
)

Expand Down Expand Up @@ -217,14 +211,14 @@ test_that("predict() params should override keyword argument for raw-score predi
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
)

# check that the predictions from predict.lgb.Booster() really look like raw score predictions
preds_prob <- predict(bst, X)
preds_raw_s3_keyword <- predict(bst, X, type = "raw")
preds_prob_from_raw <- 1.0 / (1.0 + exp(-preds_raw_s3_keyword))
expect_equal(preds_prob, preds_prob_from_raw, tolerance = TOLERANCE)
expect_equal(preds_prob, preds_prob_from_raw, tolerance = .LGB_NUMERIC_TOLERANCE)
accuracy <- sum(as.integer(preds_prob_from_raw > 0.5) == y) / length(y)
expect_equal(accuracy, 1.0)

Expand Down Expand Up @@ -269,7 +263,7 @@ test_that("predict() params should override keyword argument for leaf-index pred
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
)

# check that predictions really look like leaf index predictions
Expand Down Expand Up @@ -323,7 +317,7 @@ test_that("predict() params should override keyword argument for feature contrib
, num_threads = .LGB_MAX_THREADS
)
, nrounds = 10L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
)

# check that predictions really look like feature contributions
Expand Down Expand Up @@ -431,7 +425,7 @@ test_that("predict() keeps row names from data (regression)", {
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)
)
.check_all_row_name_expectations(bst, X)
Expand All @@ -447,7 +441,7 @@ test_that("predict() keeps row names from data (binary classification)", {
data = dtrain
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
.check_all_row_name_expectations(bst, X)
Expand All @@ -464,7 +458,7 @@ test_that("predict() keeps row names from data (multi-class classification)", {
, obj = "multiclass"
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
)
.check_all_row_name_expectations(bst, X)
})
Expand All @@ -485,7 +479,7 @@ test_that("predictions for regression and binary classification are returned as
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
Expand All @@ -503,7 +497,7 @@ test_that("predictions for regression and binary classification are returned as
data = dtrain
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
Expand All @@ -523,7 +517,7 @@ test_that("predictions for multiclass classification are returned as matrix", {
data = dtrain
, obj = "multiclass"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X)
Expand Down Expand Up @@ -668,7 +662,7 @@ test_that("predict type='class' returns predicted class for classification objec
data = dtrain
, obj = "binary"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(bst, X, type = "class")
Expand All @@ -682,7 +676,7 @@ test_that("predict type='class' returns predicted class for classification objec
data = dtrain
, obj = "multiclass"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)
)
pred <- predict(model, X, type = "class")
Expand All @@ -698,7 +692,7 @@ test_that("predict type='class' returns values in the target's range for regress
data = dtrain
, obj = "regression"
, nrounds = 5L
, verbose = VERBOSITY
, verbose = .LGB_VERBOSITY
, params = list(num_threads = .LGB_MAX_THREADS)
)
pred <- predict(bst, X, type = "class")
Expand Down