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Enable parameter validation for R. (#5569)

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* Enable parameter validation for R.

* Add test.
This commit is contained in:
Jiaming Yuan
2020-04-22 02:19:09 +08:00
committed by GitHub
parent 564b22cee5
commit c355ab65ed
4 changed files with 43 additions and 8 deletions
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45
R-package/tests/testthat/test_basic.R
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@@ -35,6 +35,40 @@ test_that("train and predict binary classification", {
expect_lt(abs(err_pred1 - err_log), 10e-6)
})
test_that("parameter validation works", {
p <- list(foo = "bar")
nrounds = 1
set.seed(1994)
d <- cbind(
x1 = rnorm(10),
x2 = rnorm(10),
x3 = rnorm(10))
y <- d[,"x1"] + d[,"x2"]^2 +
ifelse(d[,"x3"] > .5, d[,"x3"]^2, 2^d[,"x3"]) +
rnorm(10)
dtrain <- xgb.DMatrix(data=d, info = list(label=y))
correct <- function() {
params <- list(max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
objective = "reg:squarederror")
xgb.train(params = params, data = dtrain, nrounds = nrounds)
}
expect_silent(correct())
incorrect <- function() {
params <- list(max_depth = 2, booster = "dart",
rate_drop = 0.5, one_drop = TRUE,
objective = "reg:squarederror",
foo = "bar", bar = "foo")
output <- capture.output(
xgb.train(params = params, data = dtrain, nrounds = nrounds))
print(output)
}
expect_output(incorrect(), "bar, foo")
})
test_that("dart prediction works", {
nrounds = 32
set.seed(1994)
@@ -68,7 +102,6 @@ test_that("dart prediction works", {
one_drop = TRUE,
nthread = 1,
tree_method= "exact",
verbosity = 3,
objective = "reg:squarederror"
),
data = dtrain,
@@ -324,15 +357,15 @@ test_that("colsample_bytree works", {
test_y <- as.numeric(rowSums(test_x) > 0)
colnames(train_x) <- paste0("Feature_", sprintf("%03d", 1:100))
colnames(test_x) <- paste0("Feature_", sprintf("%03d", 1:100))
dtrain <- xgb.DMatrix(train_x, label = train_y)
dtrain <- xgb.DMatrix(train_x, label = train_y)
dtest <- xgb.DMatrix(test_x, label = test_y)
watchlist <- list(train = dtrain, eval = dtest)
# Use colsample_bytree = 0.01, so that roughly one out of 100 features is
# chosen for each tree
param <- list(max_depth = 2, eta = 0, verbosity = 0, nthread = 2,
## Use colsample_bytree = 0.01, so that roughly one out of 100 features is chosen for
## each tree
param <- list(max_depth = 2, eta = 0, nthread = 2,
colsample_bytree = 0.01, objective = "binary:logistic",
eval_metric = "auc")
set.seed(2)
set.seed(2)
bst <- xgb.train(param, dtrain, nrounds = 100, watchlist, verbose = 0)
xgb.importance(model = bst)
# If colsample_bytree works properly, a variety of features should be used