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Lint fix on consistent assignment

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terrytangyuan
2015-10-28 22:22:51 -04:00
parent ce9d7045f9
commit d7fce99564
8 changed files with 46 additions and 46 deletions
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18
R-package/tests/testthat/test_basic.R
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@@ -4,30 +4,30 @@ context("basic functions")
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train = agaricus.train
test = agaricus.test
train <- agaricus.train
test <- agaricus.test
test_that("train and predict", {
bst = xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
pred = predict(bst, test$data)
pred <- predict(bst, test$data)
})
test_that("early stopping", {
res = xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
res <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
early.stop.round = 3, maximize = FALSE)
expect_true(nrow(res)<20)
bst = xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
early.stop.round = 3, maximize = FALSE)
pred = predict(bst, test$data)
pred <- predict(bst, test$data)
})
test_that("save_period", {
bst = xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
save_period = 10, save_name = "xgb.model")
pred = predict(bst, test$data)
pred <- predict(bst, test$data)
})

4
R-package/tests/testthat/test_helpers.R
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@@ -11,8 +11,8 @@ df <- data.table(Arthritis, keep.rownames = F)
df[,AgeDiscret:= as.factor(round(Age/10,0))]
df[,AgeCat:= as.factor(ifelse(Age > 30, "Old", "Young"))]
df[,ID:=NULL]
sparse_matrix = sparse.model.matrix(Improved~.-1, data = df)
output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
output_vector <- df[,Y:=0][Improved == "Marked",Y:=1][,Y]
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")