set_model_arg {modelenv}

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)

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.

Value

A tibble

Examples

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