context("test-Lrnr_bound.R -- bound predictions")
library(origami)
g0 <- function(W) {
W1 <- W[, 1]
scale_factor <- 0.8
A <- plogis(scale_factor * W1)
}
gen_data <- function(n = 1000, p = 4) {
W <- matrix(rnorm(n * p), nrow = n)
colnames(W) <- paste("W", seq_len(p), sep = "")
g0W <- g0(W)
A <- rbinom(n, 1, g0W)
u <- runif(n)
df <- data.frame(W, A)
df$g0W <- g0(W)
return(df)
}
set.seed(1234)
data <- gen_data(1000)
Wnodes <- grep("^W", names(data), value = TRUE)
Anode <- "A"
task <- sl3_Task$new(data, covariates = Wnodes, outcome = Anode)
learners <- list(
xgb = make_learner(Lrnr_xgboost, verbose = 0),
glm_fast = make_learner(Lrnr_glm_fast),
mean = make_learner(Lrnr_mean)
)
# define Super Learner and predict without bounding
binom_sl <- make_learner(Lrnr_sl, learners)
sl_fit <- binom_sl$train(task)
preds <- sl_fit$predict()
# set up bounding learner and apply to SL in pipeline
lrnr_bound <- Lrnr_bound$new(bound = 0.1)
sl_pipeline_bounded <- make_learner(Pipeline, sl_fit, lrnr_bound)
sl_fit_bounded <- sl_pipeline_bounded$train(task)
bounded_preds <- sl_fit_bounded$predict()
test_that("Lrnr_bound is bounding predictions within given limits", {
expect_gte(min(bounded_preds), 0.1)
expect_lte(max(bounded_preds), 0.9)
})
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