Rename Agghoo class into AgghooCV (to avoid case-insensitive issues)

[agghoo.git] / R / agghoo.R

#' agghoo
#'
#' Run the agghoo procedure. (...)
#'
#' @param data Data frame or matrix containing the data in lines.
#' @param target The target values to predict. Generally a vector.
#' @param task "classification" or "regression". Default:
#'        regression if target is numerical, classification otherwise.
#' @param gmodel A "generic model", which is a function returning a predict
#'        function (taking X as only argument) from the tuple
#'        (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
#'        referring to cross-validation. Cross-validation is run on an array
#'        of 'param's. See params argument. Default: see R6::Model.
#' @param params A list of parameters. Often, one list cell is just a
#'        numerical value, but in general it could be of any type.
#'        Default: see R6::Model.
#' @param quality A function assessing the quality of a prediction.
#'        Arguments are y1 and y2 (comparing a prediction to known values).
#'        Default: see R6::AgghooCV.
#'
#' @return An R6::AgghooCV object.
#'
#' @examples
#' # Regression:
#' a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
#' a_reg$fit()
#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
#' # Classification
#' a_cla <- agghoo(iris[,-5], iris[,5])
#' a_cla$fit(mode="standard")
#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
#'
#' @export
agghoo <- function(data, target, task = NA, gmodel = NA, params = NA, quality = NA) {
	# Args check:
  if (!is.data.frame(data) && !is.matrix(data))
    stop("data: data.frame or matrix")
  if (nrow(data) <= 1 || any(dim(data) == 0))
    stop("data: non-empty, >= 2 rows")
  if (!is.numeric(target) && !is.factor(target) && !is.character(target))
    stop("target: numeric, factor or character vector")
  if (!is.na(task))
    task = match.arg(task, c("classification", "regression"))
  if (is.character(gmodel))
    gmodel <- match.arg("knn", "ppr", "rf")
  else if (!is.na(gmodel) && !is.function(gmodel))
    # No further checks here: fingers crossed :)
    stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
  if (is.numeric(params) || is.character(params))
    params <- as.list(params)
  if (!is.na(params) && !is.list(params))
    stop("params: numerical, character, or list (passed to model)")
  if (!is.na(gmodel) && !is.character(gmodel) && is.na(params))
    stop("params must be provided when using a custom model")
  if (is.na(gmodel) && !is.na(params))
    stop("model must be provided when using custom params")
  if (!is.na(quality) && !is.function(quality))
    # No more checks here as well... TODO:?
    stop("quality: function(y1, y2) --> Real")

  if (is.na(task)) {
    if (is.numeric(target))
      task = "regression"
    else
      task = "classification"
  }
  # Build Model object (= list of parameterized models)
  model <- Model$new(data, target, task, gmodel, params)
  # Return AgghooCV object, to run and predict
  AgghooCV$new(data, target, task, model, quality)
}

#' compareToStandard
#'
#' Temporary function to compare agghoo to CV
#' (TODO: extended, in another file, more tests - when faster code).
#'
#' @export
compareToStandard <- function(df, t_idx, task = NA, rseed = -1) {
  if (rseed >= 0)
    set.seed(rseed)
  if (is.na(task))
    task <- ifelse(is.numeric(df[,t_idx]), "regression", "classification")
  n <- nrow(df)
  test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
  a <- agghoo(df[-test_indices,-t_idx], df[-test_indices,t_idx], task)
  a$fit(mode="agghoo") #default mode
  pa <- a$predict(df[test_indices,-t_idx])
  print(paste("error agghoo",
              ifelse(task == "classification",
                     mean(p != df[test_indices,t_idx]),
                     mean(abs(pa - df[test_indices,t_idx])))))
  # Compare with standard cross-validation:
  a$fit(mode="standard")
  ps <- a$predict(df[test_indices,-t_idx])
  print(paste("error CV",
              ifelse(task == "classification",
                     mean(ps != df[test_indices,t_idx]),
                     mean(abs(ps - df[test_indices,t_idx])))))
}