R/explain_mlr.R

In DALEXtra: Extension for 'DALEX' Package

#' Create explainer from your mlr model
#'
#' DALEX is designed to work with various black-box models like tree ensembles, linear models, neural networks etc.
#' Unfortunately R packages that create such models are very inconsistent. Different tools use different interfaces to train, validate and use models.
#' One of those tools, which is one of the most popular one is the mlr package. We would like to present dedicated explain function for it.
#'
#'
#' @inheritParams DALEX::explain
#'
#' @return explainer object (\code{\link[DALEX]{explain}}) ready to work with DALEX
#'
#' @import DALEX
#' @importFrom stats predict
#' @importFrom DALEX yhat
#'
#' @rdname explain_mlr
#' @export
#' @examples
#' library("DALEXtra")
#' titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
#' titanic_train <- read.csv(system.file("extdata", "titanic_train.csv", package = "DALEXtra"))
#' library("mlr")
#' task <- mlr::makeClassifTask(
#' id = "R",
#' data = titanic_train,
#' target = "survived"
#' )
#' learner <- mlr::makeLearner(
#'   "classif.gbm",
#'   par.vals = list(
#'     distribution = "bernoulli",
#'     n.trees = 500,
#'     interaction.depth = 4,
#'     n.minobsinnode = 12,
#'     shrinkage = 0.001,
#'     bag.fraction = 0.5,
#'     train.fraction = 1
#'   ),
#'   predict.type = "prob"
#' )
#' gbm <- mlr::train(learner, task)
#' explain_mlr(gbm, titanic_test[,1:17], titanic_test[,18])
#'


explain_mlr <-
  function(model,
           data = NULL,
           y = NULL,
           weights = NULL,
           predict_function = NULL,
           predict_function_target_column = NULL,
           residual_function = NULL,
           ...,
           label = NULL,
           verbose = TRUE,
           precalculate = TRUE,
           colorize = !isTRUE(getOption('knitr.in.progress')),
           model_info = NULL,
           type = NULL) {
    explain(
      model,
      data = data,
      y = y,
      weights = weights,
      predict_function = predict_function,
      predict_function_target_column = predict_function_target_column,
      residual_function = residual_function,
      ...,
      label = label,
      verbose = verbose,
      precalculate = precalculate,
      colorize = colorize,
      model_info = model_info,
      type = type
    )



  }