Closed
Description
# nearest_neighbor() demo showing that for datasets with factor
# predictors, kknn package must be loaded manually
library(tidyverse)
library("tidymodels")
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
#> -- Attaching packages -------------------------------------------------------------------------------- tidymodels 0.0.3 --
#> v broom 0.5.4 v recipes 0.1.9
#> v dials 0.0.4 v rsample 0.0.5
#> v infer 0.5.1 v yardstick 0.0.5
#> v parsnip 0.0.5.9000
#> -- Conflicts ----------------------------------------------------------------------------------- tidymodels_conflicts() --
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# First, all predictors are numeric:
mtcars_numeric <- mtcars %>% mutate(am = as_factor(am))
# nearest_neighbor() works fine:
mtcars_knn <-
nearest_neighbor() %>%
set_mode("classification") %>%
set_engine("kknn") %>%
parsnip::fit(am ~ ., data = mtcars_numeric)
# Nextd, add a factor predictor:
mtcars_with_factor <- mtcars %>%
mutate(am = as_factor(am),
cyl = as_factor(cyl))
# nearest_neighbor throws an error:
mtcars_knn <-
nearest_neighbor() %>%
set_mode("classification") %>%
set_engine("kknn") %>%
parsnip::fit(am ~ ., data = mtcars_with_factor)
#> Error in get(ctr, mode = "function", envir = parent.frame()): object 'contr.dummy' of mode 'function' was not found
#> Timing stopped at: 0.01 0 0.02
# Now manually load kknn package, rerun above code & it works fine:
library("kknn")
mtcars_knn <-
nearest_neighbor() %>%
set_mode("classification") %>%
set_engine("kknn") %>%
parsnip::fit(am ~ ., data = mtcars_with_factor)
devtools::session_info()
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#> date 2020-02-20
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#> [1] C:/Users/BobMu/OneDrive/Documents/R/win-library/3.6
#> [2] C:/Program Files/R/R-3.6.2/libraryCreated on 2020-02-20 by the reprex package (v0.3.0)