Nothing

```
require(xgboost)
context("interaction constraints")
set.seed(1024)
x1 <- rnorm(1000, 1)
x2 <- rnorm(1000, 1)
x3 <- sample(c(1, 2, 3), size = 1000, replace = TRUE)
y <- x1 + x2 + x3 + x1 * x2 * x3 + rnorm(1000, 0.001) + 3 * sin(x1)
train <- matrix(c(x1, x2, x3), ncol = 3)
test_that("interaction constraints for regression", {
# Fit a model that only allows interaction between x1 and x2
bst <- xgboost(data = train, label = y, max_depth = 3,
eta = 0.1, nthread = 2, nrounds = 100, verbose = 0,
interaction_constraints = list(c(0, 1)))
# Set all observations to have the same x3 values then increment
# by the same amount
preds <- lapply(c(1, 2, 3), function(x){
tmat <- matrix(c(x1, x2, rep(x, 1000)), ncol = 3)
return(predict(bst, tmat))
})
# Check incrementing x3 has the same effect on all observations
# since x3 is constrained to be independent of x1 and x2
# and all observations start off from the same x3 value
diff1 <- preds[[2]] - preds[[1]]
test1 <- all(abs(diff1 - diff1[1]) < 1e-4)
diff2 <- preds[[3]] - preds[[2]]
test2 <- all(abs(diff2 - diff2[1]) < 1e-4)
expect_true({
test1 & test2
}, "Interaction Contraint Satisfied")
})
test_that("interaction constraints scientific representation", {
rows <- 10
## When number exceeds 1e5, R paste function uses scientific representation.
## See: https://github.com/dmlc/xgboost/issues/5179
cols <- 1e5 + 10
d <- matrix(rexp(rows, rate = .1), nrow = rows, ncol = cols)
y <- rnorm(rows)
dtrain <- xgb.DMatrix(data = d, info = list(label = y))
inc <- list(c(seq.int(from = 0, to = cols, by = 1)))
with_inc <- xgb.train(data = dtrain, tree_method = 'hist',
interaction_constraints = inc, nrounds = 10)
without_inc <- xgb.train(data = dtrain, tree_method = 'hist', nrounds = 10)
expect_equal(xgb.save.raw(with_inc), xgb.save.raw(without_inc))
})
```

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