Model handler functions for API endpoint

These are developer-facing functions, useful for supporting new model types. Each model supported by vetiver_model() uses two handler functions in vetiver_api():

• The handler_startup function executes when the API starts. Use this function for tasks like loading packages. A model can use the default method here, which is NULL (to do nothing at startup).

• The handler_predict function executes at each API call. Use this function for calling predict() and any other tasks that must be executed at each API call.

Usage

# S3 method for class 'train'
handler_startup(vetiver_model)

# S3 method for class 'train'
handler_predict(vetiver_model, ...)

# S3 method for class 'gam'
handler_startup(vetiver_model)

# S3 method for class 'gam'
handler_predict(vetiver_model, ...)

# S3 method for class 'glm'
handler_predict(vetiver_model, ...)

handler_startup(vetiver_model)

# Default S3 method
handler_startup(vetiver_model)

handler_predict(vetiver_model, ...)

# Default S3 method
handler_predict(vetiver_model, ...)

# S3 method for class 'keras.engine.training.Model'
handler_startup(vetiver_model)

# S3 method for class 'keras.engine.training.Model'
handler_predict(vetiver_model, ...)

# S3 method for class 'kproto'
handler_predict(vetiver_model, ...)

# S3 method for class 'lm'
handler_predict(vetiver_model, ...)

# S3 method for class 'luz_module_fitted'
handler_startup(vetiver_model)

# S3 method for class 'luz_module_fitted'
handler_predict(vetiver_model, ...)

# S3 method for class 'Learner'
handler_startup(vetiver_model)

# S3 method for class 'Learner'
handler_predict(vetiver_model, ...)

# S3 method for class 'int_conformal_split'
handler_startup(vetiver_model)

# S3 method for class 'int_conformal_split'
handler_predict(vetiver_model, ...)

# S3 method for class 'int_conformal_full'
handler_startup(vetiver_model)

# S3 method for class 'int_conformal_full'
handler_predict(vetiver_model, ...)

# S3 method for class 'int_conformal_quantile'
handler_startup(vetiver_model)

# S3 method for class 'int_conformal_quantile'
handler_predict(vetiver_model, ...)

# S3 method for class 'int_conformal_cv'
handler_startup(vetiver_model)

# S3 method for class 'int_conformal_cv'
handler_predict(vetiver_model, ...)

# S3 method for class 'ranger'
handler_startup(vetiver_model)

# S3 method for class 'ranger'
handler_predict(vetiver_model, ...)

# S3 method for class 'recipe'
handler_startup(vetiver_model)

# S3 method for class 'recipe'
handler_predict(vetiver_model, ...)

# S3 method for class 'model_stack'
handler_startup(vetiver_model)

# S3 method for class 'model_stack'
handler_predict(vetiver_model, ...)

# S3 method for class 'workflow'
handler_startup(vetiver_model)

# S3 method for class 'workflow'
handler_predict(vetiver_model, ...)

# S3 method for class 'xgb.Booster'
handler_startup(vetiver_model)

# S3 method for class 'xgb.Booster'
handler_predict(vetiver_model, ...)

Arguments

vetiver_model

A deployable vetiver_model() object

...

Other arguments passed to predict(), such as prediction type

Value

A handler_startup function should return invisibly, while a handler_predict function should return a function with the signature function(req). The request body (req$body) consists of the new data at prediction time; this function should return predictions either as a tibble or as a list coercable to a tibble via tibble::as_tibble().

Details

These are two generics that use the class of vetiver_model$model for dispatch.

Examples

cars_lm <- lm(mpg ~ ., data = mtcars)
v <- vetiver_model(cars_lm, "cars_linear")
handler_startup(v)
handler_predict(v)
#> function (req) 
#> {
#>     newdata <- req$body
#>     if (!is_null(ptype)) {
#>         newdata <- vetiver_type_convert(newdata, ptype)
#>         newdata <- hardhat::scream(newdata, ptype)
#>     }
#>     ret <- predict(vetiver_model$model, newdata = newdata, ...)
#>     list(.pred = ret)
#> }
#> <bytecode: 0x55f2b48fdff0>
#> <environment: 0x55f2b48e4b38>