R/prepareTransactions.R

In ianjjohnson/arulesCBA: Classification Based on Association Rules

#' Prepare Data for Associative Classification
#'
#' Data in a data.frame are discretized using class-based
#' discretization and converted into transactions. For transaction data that was not created
#' from a data.frame, a negative class item is added to create data for a binary classifier.
#'
#' @param formula the formula.
#' @param data a data.frame with the data.
#' @param disc.method Discretization method used to discretize continuous
#' variables if data is a data.frame (default: \code{"mdlp"}). See
#' \code{\link{discretizeDF.supervised}} for more supervised discretization
#' methods.
#' @param match typically \code{NULL}. Only used internally if data is a
#' already a set of transactions.
#' @return An object of class \code{\link[arules]{transactions}} from
#' \pkg{arules} with an attribute called \code{"disc_info"} that contains
#' information on the used discretization for each column.
#' @author Michael Hahsler
#' @seealso \code{\link[arules]{transactions}}, \code{\link{transactions2DF}}.
#' @examples
#'
#' # Perform discretization and convert to transactions
#' data("iris")
#' iris_trans <- prepareTransactions(Species ~ ., iris)
#' inspect(head(iris_trans))
#'
#' # A negative class item is added for regular transaction data (here "!canned beer")
#' # Note: backticks are needed in formulas with item labels that contain a space.
#' data("Groceries")
#' g2 <- prepareTransactions(`canned beer` ~ ., Groceries)
#' inspect(head(g2))
#'
prepareTransactions <- function(formula, data, disc.method = "mdlp", match = NULL) {


  if(is(data, "transactions")) {
    ### add negative items to handle regular transaction data without variable info
    if(is.null(itemInfo(data)$variables))
      data <- addComplement(data, all.vars(formula)[1])

    ### Note: transactions might need recoding!
    if(!is.null(match)) return(recode(data, itemLabels = itemLabels(match)))
    else return(data)
  }

  # handle logical variables by making them into factors (so FALSE is preserved)
  if(!is(data, "transactions")) {
    for (i in 1:ncol(data))
      if(is.logical(data[[i]]))
        data[[i]] <- factor(data[[i]], levels = c(TRUE, FALSE))
  }
  # disc.method is a character string with the method
  if(!is.list(disc.method)) {

    disc_info <- NULL
    data <- discretizeDF.supervised(formula, data, method = disc.method)
    disc_info <- lapply(data, attr, "discretized:breaks")

    data <- as(data, "transactions")
    attr(data, "disc_info") <- disc_info

    return(data)
  }

  ### disc is a list with discretization info
    data <- discretizeDF(data, lapply(disc.method,
      FUN = function(x) list(method = "fixed", breaks = x)))
    data <- as(data, "transactions")

  return(data)
}