modelenv (version 0.2.0)

set_model_arg: Register Argument for Model

Description

This function is used to register argument information for a model and engine combination.

Usage

set_model_arg(model, eng, exposed, original, func, has_submodel)
get_model_arg(model, eng)

Value

A tibble

Arguments

model

A single character string for the model type (e.g. "k_means", etc).

eng

A single character string for the model engine.

exposed

A single character string for the "harmonized" argument name that the modeling function exposes.

original

A single character string for the argument name that underlying model function uses.

func

A named character vector that describes how to call a function. func should have elements pkg and fun. The former is optional but is recommended and the latter is required. For example, c(pkg = "stats", fun = "lm") would be used to invoke the usual linear regression function. In some cases, it is helpful to use c(fun = "predict") when using a package's predict method.

has_submodel

A single logical for whether the argument can make predictions on multiple submodels at once.

Details

This function needs to be called once for each argument that you are exposing.

Examples

if (FALSE) {
set_new_model("shallow_learning_model")
set_model_mode("shallow_learning_model", "partition")
set_model_engine("shallow_learning_model", "partition", "stats")

set_model_arg(
  model = "shallow_learning_model",
  eng = "stats",
  exposed = "method",
  original = "method",
  func = list(pkg = "stats", fun = "lm"),
  has_submodel = FALSE
)

get_model_arg("shallow_learning_model", "stats")
get_model_arg("shallow_learning_model", "stats")$func
}