juliasilge
commented
1 year ago You can definitely do this, yes! To make it work, you'll need to wrap your head around how vetiver dispatches off of classes. You might want to take a look at the code for lm() and the code for tidymodels to see more details.
To get started, you need to add a class to your function:
my_ported_model <- function(df) {
df$x + 2 * df$y + abs(df$z)
}
class(my_ported_model) <- c("ported_model", class(my_ported_model))
class(my_ported_model)
#> [1] "ported_model" "function"You probably also want to make a predict method:
predict.ported_model <- function(object, newdata) {
my_ported_model(newdata)
}Next you need to make the methods for your new model type. At a minimum you need these three (but you might also look at the links I included above for things like how to track needed packages):
library(vetiver)
vetiver_create_description.ported_model <- function(model) {
"A model that I ported from SAS"
}
vetiver_ptype.ported_model <- function(model, ...) {
vctrs::vec_ptype(tibble::tibble(x = integer(), y = integer(), z = integer()))
}
handler_predict.ported_model <- function(vetiver_model, ...) {
ptype <- vetiver_model$prototype
function(req) {
newdata <- req$body
newdata <- vetiver_type_convert(newdata, ptype)
newdata <- hardhat::scream(newdata, ptype)
ret <- predict(vetiver_model$model, newdata = newdata, ...)
list(.pred = ret)
}
}You can put that all together and make your vetiver model object:
v <- vetiver_model(my_ported_model, "my-simple-model")
v
#>
#> ── my-simple-model ─ <ported_model> model for deployment
#> A model that I ported from SAS using 3 featuresYou can interact with the API this created like so:
library(plumber)
pr() |>
vetiver_api(v)
#> # Plumber router with 3 endpoints, 4 filters, and 1 sub-router.
#> # Use `pr_run()` on this object to start the API.
#> ├──[queryString]
#> ├──[body]
#> ├──[cookieParser]
#> ├──[sharedSecret]
#> ├──/logo
#> │ │ # Plumber static router serving from directory: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/vetiver
#> ├──/metadata (GET)
#> ├──/ping (GET)
#> └──/predict (POST)Created on 2023-06-06 with reprex v2.0.2
If you then pipe to pr_run() you can interact with it locally:
All that being said, it might be easier to not use vetiver at all and write your function into a plumber file directly. I think it depends on how helpful the other pieces of vetiver are in your use case, like versioning as a pin (this would work fine using what I outlined above) and monitoring.
aminadibi
rsh52
Thanks for the great work on this package.
Question:
vetiver_models()expects a model object as a parameter. In our organization we have a large number of legacy models trained in SAS. Suppose we have an equation that came from fitting a model outside R, and we have an R function that makes predictions based on that equation. Is it possible to manually create a model object based on that function, so that the model trained outside R can be deployed and monitored with vetiver?Thanks