test_that("kknn + predict() works", {
skip_on_cran()
skip_if_not_installed("kknn")
suppressPackageStartupMessages(library(kknn))
m <- dim(iris)[1]
val <- sample(1:m,
size = round(m/3),
replace = FALSE,
prob = rep(1/m, m))
iris.learn <- iris[-val,]
iris.valid <- iris[val,]
kknn_fit <- kknn(Species ~ .,
iris.learn,
iris.valid,
distance = 1,
kernel = "triangular")
x <- axe_call(kknn_fit)
expect_equal(x$call, rlang::expr(dummy_call()))
x <- axe_env(kknn_fit)
expect_identical(attr(x$terms, ".Environment"), rlang::base_env())
x <- axe_fitted(kknn_fit)
expect_equal(x$fitted.values, list(NULL))
x <- butcher(kknn_fit)
new_data <- data.frame(iris[c(1,10, 13), 1:4])
expect_equal(predict(x, new_data, type = "prob"),
predict(kknn_fit, new_data, type = "prob"))
expect_error(predict(x, new_data))
})
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