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