set_pred: Register Prediction Method for Model

In modelenv: Provide Tools to Register Models for Use in 'tidymodels'

Register Prediction Method for Model

Description

This function is used to register prediction method information for a model, mode, and engine combination.

Usage

set_pred(model, mode, eng, type, value)

get_pred_type(model, type)

Arguments

model	A single character string for the model type (e.g. "k_means", etc).
mode	A single character string for the model mode (e.g. "partition").
eng	A single character string for the model engine.
type	A single character value for the type of prediction. Possible values are: cluster and raw.
value	A list of values, described in the Details.

Details

The list passed to value needs the following values:

• pre and post are optional functions that can preprocess the data being fed to the prediction code and to postprocess the raw output of the predictions. These won’t be needed for this example, but a section below has examples of how these can be used when the model code is not easy to use. If the data being predicted has a simple type requirement, you can avoid using a pre function with the args below.

• func is the prediction function (in the same format as above). In many cases, packages have a predict method for their model’s class but this is typically not exported. In this case (and the example below), it is simple enough to make a generic call to predict() with no associated package.

• args is a list of arguments to pass to the prediction function. These will most likely be wrapped in rlang::expr() so that they are not evaluated when defining the method. For mda, the code would be predict(object, newdata, type = "class"). What is actually given to the function is the model fit object, which includes a sub-object called fit() that houses the mda model object. If the data need to be a matrix or data frame, you could also use newdata = quote(as.data.frame(newdata)) or similar.

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_pred(
  model = "shallow_learning_model",
  eng = "stats",
  mode = "partition",
  type = "cluster",
  value = list(
    pre = NULL,
    post = NULL,
    func = c(fun = "predict"),
    args =
      list(
        object = rlang::expr(object$fit),
        newdata = rlang::expr(new_data),
        type = "response"
      )
  )
)

get_pred_type("shallow_learning_model", "cluster")
get_pred_type("shallow_learning_model", "cluster")$value

modelenv documentation built on March 31, 2023, 10:31 p.m.