9254c58e4d
* add interaction constraints * enable both interaction and monotonic constraints at the same time * fix lint * add R test, fix lint, update demo * Use dmlc::JSONReader to express interaction constraints as nested lists; Use sparse arrays for bookkeeping * Add Python test for interaction constraints * make R interaction constraints parameter based on feature index instead of column names, fix R coding style * Fix lint * Add BlueTea88 to CONTRIBUTORS.md * Short circuit when no constraint is specified; address review comments * Add tutorial for feature interaction constraints * allow interaction constraints to be passed as string, remove redundant column_names argument * Fix typo * Address review comments * Add comments to Python test
39 lines
1.2 KiB
R
39 lines
1.2 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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