one-class SVM with kernlab fails to produce predictions

[aminadibi]

The problem

I'm having trouble with using parsnip for one-class SVMs with kernlab engine using type="one-svc" option.
First, it seems like I cannot get the fitted model to produce any predictions (see the reprex below). Would appreciate any help with that.

Second, unlike kernlab, it seems that the only way to fit the model with parsnip is to create a fake response column to act as the y in the formula, even though one-class novelty detection does not require a response variable. Is there any other way?

Thanks.

Reproducible example

library(tidymodels)
set.seed(200)
x1 <- rnorm(200)
x2 <- rnorm(200)+2 

df<-tibble(x1=x1, x2=x2)
df_test <- tibble(x1=x1+1, x2=x2+1)

df <- df %>% mutate(DUMMY_RESPONSE_DUMMY=as.factor(rep(9999,nrow(df))))

svm_rbf_spec <- svm_rbf() %>%
  set_mode("classification") %>%
  set_engine("kernlab", type="one-svc") 
  
svm_rbf_fit <- svm_rbf_spec %>%
  fit(DUMMY_RESPONSE_DUMMY~., data=df)

predict(svm_rbf_fit, new_data = df_test)
#> Error in res$values: $ operator is invalid for atomic vectors

^{Created on 2023-05-25 with reprex v2.0.2}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.0 (2023-04-21 ucrt)
#>  os       Windows 11 x64 (build 22621)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_Canada.utf8
#>  ctype    English_Canada.utf8
#>  tz       America/Vancouver
#>  date     2023-05-25
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
#>  backports      1.4.1      2021-12-13 [1] CRAN (R 4.3.0)
#>  broom        * 1.0.4      2023-03-11 [1] CRAN (R 4.3.0)
#>  class          7.3-21     2023-01-23 [2] CRAN (R 4.3.0)
#>  cli            3.6.1      2023-03-23 [1] CRAN (R 4.3.0)
#>  codetools      0.2-19     2023-02-01 [2] CRAN (R 4.3.0)
#>  colorspace     2.1-0      2023-01-23 [1] CRAN (R 4.3.0)
#>  data.table     1.14.8     2023-02-17 [1] CRAN (R 4.3.0)
#>  dials        * 1.2.0      2023-04-03 [1] CRAN (R 4.3.0)
#>  DiceDesign     1.9        2021-02-13 [1] CRAN (R 4.3.0)
#>  digest         0.6.31     2022-12-11 [1] CRAN (R 4.3.0)
#>  dplyr        * 1.1.2      2023-04-20 [1] CRAN (R 4.3.0)
#>  evaluate       0.21       2023-05-05 [1] CRAN (R 4.3.0)
#>  fansi          1.0.4      2023-01-22 [1] CRAN (R 4.3.0)
#>  fastmap        1.1.1      2023-02-24 [1] CRAN (R 4.3.0)
#>  foreach        1.5.2      2022-02-02 [1] CRAN (R 4.3.0)
#>  fs             1.6.2      2023-04-25 [1] CRAN (R 4.3.0)
#>  furrr          0.3.1      2022-08-15 [1] CRAN (R 4.3.0)
#>  future         1.32.0     2023-03-07 [1] CRAN (R 4.3.0)
#>  future.apply   1.11.0     2023-05-21 [1] CRAN (R 4.3.0)
#>  generics       0.1.3      2022-07-05 [1] CRAN (R 4.3.0)
#>  ggplot2      * 3.4.2      2023-04-03 [1] CRAN (R 4.3.0)
#>  globals        0.16.2     2022-11-21 [1] CRAN (R 4.3.0)
#>  glue           1.6.2      2022-02-24 [1] CRAN (R 4.3.0)
#>  gower          1.0.1      2022-12-22 [1] CRAN (R 4.3.0)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.3.0)
#>  gtable         0.3.3      2023-03-21 [1] CRAN (R 4.3.0)
#>  hardhat        1.3.0      2023-03-30 [1] CRAN (R 4.3.0)
#>  htmltools      0.5.5      2023-03-23 [1] CRAN (R 4.3.0)
#>  infer        * 1.0.4      2022-12-02 [1] CRAN (R 4.3.0)
#>  ipred          0.9-14     2023-03-09 [1] CRAN (R 4.3.0)
#>  iterators      1.0.14     2022-02-05 [1] CRAN (R 4.3.0)
#>  kernlab        0.9-32     2023-01-31 [1] CRAN (R 4.3.0)
#>  knitr          1.43       2023-05-25 [1] CRAN (R 4.3.0)
#>  lattice        0.21-8     2023-04-05 [2] CRAN (R 4.3.0)
#>  lava           1.7.2.1    2023-02-27 [1] CRAN (R 4.3.0)
#>  lhs            1.1.6      2022-12-17 [1] CRAN (R 4.3.0)
#>  lifecycle      1.0.3      2022-10-07 [1] CRAN (R 4.3.0)
#>  listenv        0.9.0      2022-12-16 [1] CRAN (R 4.3.0)
#>  lubridate      1.9.2      2023-02-10 [1] CRAN (R 4.3.0)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.3.0)
#>  MASS           7.3-58.4   2023-03-07 [2] CRAN (R 4.3.0)
#>  Matrix         1.5-4      2023-04-04 [2] CRAN (R 4.3.0)
#>  modeldata    * 1.1.0      2023-01-25 [1] CRAN (R 4.3.0)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.3.0)
#>  nnet           7.3-18     2022-09-28 [2] CRAN (R 4.3.0)
#>  parallelly     1.35.0     2023-03-23 [1] CRAN (R 4.3.0)
#>  parsnip      * 1.1.0      2023-04-12 [1] CRAN (R 4.3.0)
#>  pillar         1.9.0      2023-03-22 [1] CRAN (R 4.3.0)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.3.0)
#>  prodlim        2023.03.31 2023-04-02 [1] CRAN (R 4.3.0)
#>  purrr        * 1.0.1      2023-01-10 [1] CRAN (R 4.3.0)
#>  R.cache        0.16.0     2022-07-21 [1] CRAN (R 4.3.0)
#>  R.methodsS3    1.8.2      2022-06-13 [1] CRAN (R 4.3.0)
#>  R.oo           1.25.0     2022-06-12 [1] CRAN (R 4.3.0)
#>  R.utils        2.12.2     2022-11-11 [1] CRAN (R 4.3.0)
#>  R6             2.5.1      2021-08-19 [1] CRAN (R 4.3.0)
#>  Rcpp           1.0.10     2023-01-22 [1] CRAN (R 4.3.0)
#>  recipes      * 1.0.6      2023-04-25 [1] CRAN (R 4.3.0)
#>  reprex         2.0.2      2022-08-17 [1] CRAN (R 4.3.0)
#>  rlang          1.1.1      2023-04-28 [1] CRAN (R 4.3.0)
#>  rmarkdown      2.21       2023-03-26 [1] CRAN (R 4.3.0)
#>  rpart          4.1.19     2022-10-21 [2] CRAN (R 4.3.0)
#>  rsample      * 1.1.1      2022-12-07 [1] CRAN (R 4.3.0)
#>  rstudioapi     0.14       2022-08-22 [1] CRAN (R 4.3.0)
#>  scales       * 1.2.1      2022-08-20 [1] CRAN (R 4.3.0)
#>  sessioninfo    1.2.2      2021-12-06 [1] CRAN (R 4.3.0)
#>  styler         1.10.0     2023-05-24 [1] CRAN (R 4.3.0)
#>  survival       3.5-5      2023-03-12 [2] CRAN (R 4.3.0)
#>  tibble       * 3.2.1      2023-03-20 [1] CRAN (R 4.3.0)
#>  tidymodels   * 1.1.0      2023-05-01 [1] CRAN (R 4.3.0)
#>  tidyr        * 1.3.0      2023-01-24 [1] CRAN (R 4.3.0)
#>  tidyselect     1.2.0      2022-10-10 [1] CRAN (R 4.3.0)
#>  timechange     0.2.0      2023-01-11 [1] CRAN (R 4.3.0)
#>  timeDate       4022.108   2023-01-07 [1] CRAN (R 4.3.0)
#>  tune         * 1.1.1      2023-04-11 [1] CRAN (R 4.3.0)
#>  utf8           1.2.3      2023-01-31 [1] CRAN (R 4.3.0)
#>  vctrs          0.6.2      2023-04-19 [1] CRAN (R 4.3.0)
#>  withr          2.5.0      2022-03-03 [1] CRAN (R 4.3.0)
#>  workflows    * 1.1.3      2023-02-22 [1] CRAN (R 4.3.0)
#>  workflowsets * 1.0.1      2023-04-06 [1] CRAN (R 4.3.0)
#>  xfun           0.39       2023-04-20 [1] CRAN (R 4.3.0)
#>  yaml           2.3.7      2023-01-23 [1] CRAN (R 4.3.0)
#>  yardstick    * 1.2.0      2023-04-21 [1] CRAN (R 4.3.0)
#> 
#>  [1] C:/Users/amin/AppData/Local/R/win-library/4.3
#>  [2] C:/Program Files/R/R-4.3.0/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

[EmilHvitfeldt]

Hello @aminadibi 👋

Thanks for reporting this bug!

This bug happens in predict_class.model_fit() where

parsnip/R/predict_class.R

Line 31 in 145bac2

res <- eval_tidy(pred_call)

returns a 1 x n logical matrix.

This object doens't have an $values field so this line errors

parsnip/R/predict_class.R

Line 47 in 145bac2

res$values <- factor(as.character(res$values), levels = object$lvl)

[simonpcouch]

Do you think this is in-scope for tidyclust, @EmilHvitfeldt?

[EmilHvitfeldt]

I don't know if I would but it under clustering. It feels much close in something like applicable. Like it is a type of anomaly detection, like tidymodels/applicable#19 right?