Nothing
test_that("PipeOpCrankCompositor - basic properties", {
expect_pipeop(PipeOpCrankCompositor$new())
expect_equal(PipeOpCrankCompositor$new()$param_set$values$method, "sum_haz")
})
set.seed(2218L)
task = tgen("simsurv")$generate(20L)
test_that("PipeOpCrankCompositor - estimate", {
gr = mlr3pipelines::ppl("crankcompositor", lrn("surv.coxph"), method = "mode", which = 1)
suppressWarnings(gr$train(task))
p = gr$predict(task)[[1]]
expect_prediction_surv(p)
expect_true("crank" %in% p$predict_types)
})
test_that("no params", {
po = PipeOpCrankCompositor$new(param_vals = list())
p = po$predict(
list(lrn("surv.kaplan")$train(task)$predict(task)))$output
expect_prediction_surv(p)
expect_equal(p$lp, rep(NA_real_, 20))
})
test_that("response", {
po = PipeOpCrankCompositor$new(
param_vals = list(response = TRUE, method = "median")
)
p = po$predict(
list(lrn("surv.coxph")$train(task)$predict(task))
)$output
ignore = is.na(unlist(as.numeric(p$distr$median())))
expect_equal(p$response[ignore], numeric(sum(ignore)))
expect_equal(p$response[!ignore], unlist(as.numeric(p$distr$median()))[!ignore])
p = pipeline_crankcompositor(lrn("surv.coxph"), response = TRUE,
graph_learner = TRUE, method = "median")$train(task)$predict(task)
ignore = is.na(unlist(as.numeric(p$distr$median())))
expect_equal(p$response[ignore], numeric(sum(ignore)))
expect_equal(p$response[!ignore], unlist(as.numeric(p$distr$median()))[!ignore])
})
test_that("overwrite crank", {
pl = mlr3pipelines::ppl("crankcompositor",
lrn("surv.kaplan"),
method = "median",
graph_learner = TRUE)
p1 = pl$train(task)$predict(task)
p2 = lrn("surv.kaplan")$train(task)$predict(task)
expect_true(all(p1$crank == p2$crank))
pl = mlr3pipelines::ppl("crankcompositor",
lrn("surv.kaplan"),
graph_learner = TRUE,
overwrite = TRUE)
p1 = pl$train(task)$predict(task)
p2 = lrn("surv.kaplan")$train(task)$predict(task)
expect_false(all(p1$crank == p2$crank))
})
test_that("overwrite response", {
p = lrn("surv.kaplan")$train(task)$predict(task)
p1 = PredictionSurv$new(task = task, crank = p$crank, distr = p$distr, response = rexp(20, 0.5))
po = PipeOpCrankCompositor$new(param_vals = list(response = TRUE, overwrite = FALSE))
p2 = po$predict(list(p1))[[1]]
expect_true(all(p1$response == p2$response))
p1 = PredictionSurv$new(task = task, crank = p$crank, distr = p$distr, response = rexp(20, 0.5))
po = PipeOpCrankCompositor$new(param_vals = list(response = TRUE, overwrite = TRUE))
p2 = po$predict(list(p1))[[1]]
expect_false(all(p1$response == p2$response))
})
test_that("neg crank", {
pl = mlr3pipelines::ppl("crankcompositor",
lrn("surv.kaplan"),
method = "sum_haz",
graph_learner = TRUE,
overwrite = TRUE)
p = pl$train(task)$predict(task)
expect_true(all(p$crank < 0))
expect_true(p$score() >= 0.5)
})
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