Nothing
test_that("Construction", {
task = tsk("boston_housing")
p = PredictionRegr$new(row_ids = task$row_ids, truth = task$truth(), response = task$truth())
expect_prediction(p)
expect_prediction_regr(p)
})
test_that("Internally constructed Prediction", {
task = tsk("boston_housing")
lrn = lrn("regr.featureless")
lrn$predict_type = "se"
p = lrn$train(task)$predict(task)
expect_prediction(p)
expect_prediction_regr(p)
})
test_that("c", {
task = tsk("boston_housing")
lrn = lrn("regr.featureless")
lrn$predict_type = "se"
rr = resample(task, lrn, rsmp("cv", folds = 3))
pred = do.call(c, rr$predictions())
expect_prediction(pred)
expect_prediction_regr(pred)
dt = as.data.table(pred)
expect_data_table(dt, nrows = task$nrow, ncols = 4L, any.missing = FALSE)
# duplicates are detected?
p1 = get_private(rr)$.data$data$fact$prediction[[1L]]$test
p2 = get_private(rr)$.data$data$fact$prediction[[1L]]$test
p3 = c(p1, p2, keep_duplicates = FALSE)
expect_equal(sort(p1$data$row_ids), sort(p2$data$row_ids))
expect_equal(sort(p1$data$row_ids), sort(p3$data$row_ids))
expect_numeric(p3$response, len = length(p1$response), any.missing = FALSE)
expect_numeric(p3$se, len = length(p1$se), any.missing = FALSE)
})
test_that("c drops se (#250)", {
task = tsk("boston_housing")
lrn = lrn("regr.featureless")
rr = resample(task, lrn, rsmp("cv", folds = 3))
pred = do.call(c, rr$predictions())
expect_null(pred$data$se)
expect_false("se" %in% pred$predict_types)
expect_true(allMissing(pred$se))
expect_false("se" %in% names(as.data.table(pred)))
})
test_that("distr", {
skip_if_not_installed("distr6")
task = tsk("mtcars")
distr = distr6::VectorDistribution$new(
distribution = "Binomial",
params = replicate(task$nrow, list(prob = runif(1), size = 10), FALSE)
)
p = PredictionRegr$new(row_ids = task$row_ids, truth = task$truth(), distr = distr, check = TRUE)
expect_output(print(p))
expect_set_equal(p$predict_types, c("response", "se", "distr"))
expect_numeric(p$response, len = task$nrow, any.missing = FALSE)
expect_numeric(p$se, len = task$nrow, any.missing = FALSE, lower = 0)
expect_integer(p$missing, len = 0)
expect_prediction(p)
expect_prediction(c(p, p))
expect_output(print(p))
expect_set_equal(p$predict_types, c("response", "se", "distr"))
expect_numeric(p$response, len = task$nrow, any.missing = FALSE)
expect_numeric(p$se, len = task$nrow, any.missing = FALSE, lower = 0)
expect_integer(p$missing, len = 0)
})
test_that("as_prediction_regr", {
task = tsk("mtcars")
learner = lrn("regr.featureless")
p = learner$train(task)$predict(task)
tab = as.data.table(p)
p2 = as_prediction_regr(tab)
expect_equal(tab, as.data.table(p2))
})
test_that("filtering", {
task = tsk("mtcars")
p = PredictionRegr$new(row_ids = task$row_ids, truth = task$truth(), response = task$truth())
p2 = p$clone()$filter(1:3)
expect_set_equal(p$row_ids, 1:32)
expect_set_equal(p2$row_ids, 1:3)
expect_prediction(as_prediction_regr(as.data.table(p2)))
})
test_that("obs_loss", {
task = tsk("mtcars")
p = PredictionRegr$new(row_ids = task$row_ids, truth = task$truth(), response = task$truth())
m = msr("regr.mse")
loss = p$obs_loss()
expect_double(loss$regr.mse, lower = 0, any.missing = FALSE)
})
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