Support vector machines, using kernlab

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Function	Works
`tidypredict_fit()`, `tidypredict_sql()`, `parse_model()`	✔
`tidypredict_to_column()`	✔
`tidypredict_test()`	✔
`tidypredict_interval()`, `tidypredict_sql_interval()`	✗
`parsnip`	✔

How it works

Only linear (vanilladot) kernels are supported. With a linear kernel the decision function collapses to a weighted sum of the predictors, which is exactly the shape tidypredict can write out.

library(kernlab)
library(dplyr)
library(tidypredict)

model <- ksvm(
  mpg ~ wt + hp + disp,
  data = mtcars,
  kernel = "vanilladot",
  type = "eps-svr"
)
#>  Setting default kernel parameters

Under the hood

The parser multiplies the support vectors by their coefficients to recover one weight per predictor, undoes the scaling ksvm() applied while fitting, and adds the bias term.

pm <- parse_model(model)
str(pm, 2)
#> List of 2
#>  $ general:List of 4
#>   ..$ model  : chr "ksvm"
#>   ..$ version: num 2
#>   ..$ type   : chr "regression"
#>   ..$ is_glm : num 0
#>  $ terms  :List of 4
#>   ..$ :List of 4
#>   ..$ :List of 4
#>   ..$ :List of 4
#>   ..$ :List of 4
#>  - attr(*, "class")= chr [1:3] "parsed_model" "pm_regression" "list"

The parsed model is transformed into a dplyr, a.k.a. Tidy Eval, formula.

tidypredict_fit(model)
#> 35.9868850984331 + (wt * -3.52132505992066) + (hp * -0.0344671444570161) + 
#>     (disp * 0.000700491865519295)

From there, the Tidy Eval formula can be used anywhere it can be evaluated. tidypredict provides three paths:

Use directly inside dplyr, mutate(mtcars, !! tidypredict_fit(model))
Use tidypredict_to_column(model) to add it to a piped command set
Use tidypredict_sql(model, con) to retrieve the SQL statement

mtcars %>%
  tidypredict_to_column(model) %>%
  glimpse()
#> Rows: 32
#> Columns: 12
#> $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
#> $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
#> $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
#> $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
#> $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
#> $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
#> $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
#> $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
#> $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
#> $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
#> $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…
#> $ fit  <dbl> 23.081706, 22.183768, 24.687620, 21.055166, 18.093954, 20.341661,…

tidypredict_sql(model, dbplyr::simulate_mssql())
#> <SQL> ((35.9868850984331 + ([wt] * -3.52132505992066)) + ([hp] * -0.0344671444570161)) + ([disp] * 0.000700491865519295)

How it performs

tidypredict_test(model, mtcars)
#> tidypredict test results
#> Difference threshold: 1e-12
#> 
#>  All results are within the difference threshold

Classification

Binary classification models are supported, and return the probability of the second outcome level. ksvm() turns its decision values into probabilities with a fitted sigmoid, so the model has to be fitted with prob.model = TRUE for that sigmoid to exist.

df <- mtcars
df$vs <- factor(df$vs)

model_class <- ksvm(
  vs ~ wt + mpg,
  data = df,
  kernel = "vanilladot",
  type = "C-svc",
  prob.model = TRUE
)
#>  Setting default kernel parameters

tidypredict_fit(model_class)
#> 1/(1 + exp(-(-2.97721401151508 + (wt * -0.368454175940519) + 
#>     (mpg * 0.200709025600128))))

parsnip

tidypredict also supports ksvm model objects fitted via the parsnip package, using svm_linear() with the "kernlab" engine.

library(parsnip)

parsnip_model <- svm_linear(mode = "regression") |>
  set_engine("kernlab") |>
  fit(mpg ~ wt + hp, data = mtcars)

tidypredict_fit(parsnip_model)
#> 35.8783013722882 + (wt * -3.4369288942599) + (hp * -0.0345944994000946)

Limitations

Only the linear vanilladot kernel is supported. Non-linear kernels cannot be written as a single formula over the columns.
Only binary classification is supported, and it requires prob.model = TRUE.
Prediction intervals are not supported.
Non-syntactic column names are risky on the matrix interface, ksvm(x, y), because ksvm() mangles them with make.names() and keeps no record of the originals. See the supported models article for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.