one-class SVM with kernlab fails to produce predictions #974
Description
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 vectorsCreated on 2023-05-25 with reprex v2.0.2
Session info
sessioninfo::session_info()
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