Nothing

```
context("PipeOpThreshold")
test_that("threshold general", {
po_thr = po("threshold")
expect_pipeop(po_thr)
expect_true(po_thr$id == "threshold")
expect_equal(po_thr$param_set$values$thresholds, 0.5)
po_thr = po("threshold", param_vals = list(thresholds = c(0.3, 0.5)))
expect_pipeop(po_thr)
expect_true(po_thr$id == "threshold")
expect_true(all(po_thr$param_set$values$thresholds == c(0.3, 0.5)))
})
test_that("thresholding works for binary", {
po_lrn = po(lrn("classif.rpart", predict_type = "prob"))
# binary
t = tsk("german_credit")$filter(rows = 1:100)
# works with no args
po_thr = PipeOpThreshold$new()
expect_pipeop(po_thr)
gr = po_lrn %>>% po_thr
gr$train(t)
prd = gr$predict(t)
expect_prediction(prd[[1]])
expect_equal(gr$param_set$values$threshold.thresholds, 0.5)
# Same as setting threshold at 0.5
gr = po_lrn %>>% po_thr
gr$param_set$values$threshold.thresholds = 0.5
gr$train(t)
prd2 = gr$predict(t)
expect_equal(prd, prd2)
expect_true(gr$param_set$values$threshold.thresholds == 0.5)
# converges to prop.table for 0,1
pt = prop.table(table(t$truth()))
gr = po_lrn %>>% po_thr
gr$param_set$values$threshold.thresholds = 1
gr$train(t)
prd2 = gr$predict(t)
expect_true(gr$param_set$values$threshold.thresholds == 1)
expect_true(prd2$threshold.output$score() == pt[1])
gr = po_lrn %>>% po_thr
gr$param_set$values$threshold.thresholds = 0
gr$train(t)
prd2 = gr$predict(t)
expect_true(gr$param_set$values$threshold.thresholds == 0)
expect_true(prd2$threshold.output$score() == pt[2])
gr = po_lrn %>>% po_thr
gr$param_set$values$threshold.thresholds = c(1, 0)
gr$train(t)
prd2 = gr$predict(t)
expect_true(all(gr$param_set$values$threshold.thresholds == c(1, 0)))
expect_true(prd2$threshold.output$score() == pt[1])
})
test_that("thresholding works for multiclass", {
po_lrn = po(lrn("classif.rpart", predict_type = "prob"))
# multiclass
t = tsk("iris")
# works with no args
po_thr = PipeOpThreshold$new()
expect_pipeop(po_thr)
gr = po_lrn %>>% po_thr
gr$train(t)
expect_error(gr$predict(t), "only supported for binary classification")
gr$param_set$values$threshold.thresholds = c(a = 1, b = 0.4, c = 0.1)
gr$train(t)
expect_error(gr$predict(t), "permutation of")
gr$param_set$values$threshold.thresholds = c(.1, .1, .1)
gr$train(t)
prd = gr$predict(t)
expect_prediction(prd[[1]])
# works with args
po_thr = PipeOpThreshold$new(param_vals = list(thresholds = c(0.3, 0.4, 0.3)))
expect_pipeop(po_thr)
gr = po_lrn %>>% po_thr
gr$train(t)
prd = gr$predict(t)
expect_prediction(prd[[1]])
expect_true(all(gr$param_set$values$threshold.thresholds == c(0.3, 0.4, 0.3)))
# works with named args
po_thr = PipeOpThreshold$new(param_vals =
list(thresholds = c("virginica" = 0.3, "versicolor" = 0.4, "setosa" = 0.3)))
expect_pipeop(po_thr)
gr = po_lrn %>>% po_thr
gr$train(t)
prd = gr$predict(t)
expect_prediction(prd[[1]])
expect_true(all(gr$param_set$values$threshold.thresholds == c(0.3, 0.4, 0.3)))
# errors with wrong args
po_thr = PipeOpThreshold$new(param_vals = list(thresholds = c(0.3, 0.4)))
gr = po_lrn %>>% po_thr
gr$train(t)
expect_error(gr$predict(t), "must have length one or length equal to number of outcome levels")
po_thr = PipeOpThreshold$new(param_vals =
list(thresholds = c("foo" = 0.3, "versicolor" = 0.4, "setosa" = 0.3)))
gr = po_lrn %>>% po_thr
gr$train(t)
expect_error(gr$predict(t))
})
```

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