z_runAlgorithm.R

   1 #' @include b_Algorithm.R
   2
   3 algoNameDictionary = list(
   4     ew = "ExponentialWeights",
   5     kn = "KnearestNeighbors",
   6     ga = "GeneralizedAdditive",
   7     ml = "MLpoly",
   8     rt = "RegressionTree",
   9     rr = "RidgeRegression",
  10     sv = "SVMclassif"
  11 )
  12
  13 #' @title Simulate real-time predict
  14 #'
  15 #' @description Run the algorithm coded by \code{shortAlgoName} on data specified by the \code{stations} argument.
  16 #'
  17 #' @param shortAlgoName Short name of the algorithm.
  18 #' \itemize{
  19 #'   \item ew : Exponential Weights
  20 #'   \item ga : Generalized Additive Model
  21 #'   \item kn : K Nearest Neighbors
  22 #'   \item ml : MLpoly
  23 #'   \item rt : Regression Tree
  24 #'   \item rr : Ridge Regression
  25 #' }
  26 #' @param stations List of stations dataframes to consider.
  27 #' @param experts Vector of experts to consider (names).
  28 #' @param ... Additional arguments to be passed to the Algorithm object.
  29 #'
  30 #' @return A list with the following slots
  31 #' \itemize{
  32 #'   \item{data : data frame of all forecasts + measures (may contain NAs) + predictions, with date and station indices.}
  33 #'   \item{algo : object of class \code{Algorithm} (or sub-class).}
  34 #'   \item{stations : list of dataframes of stations for this run.}
  35 #'   \item{experts : character vector of experts for this run.}
  36 #' }
  37 #'
  38 #' @examples
  39 #' data(stations)
  40 #' r = runAlgorithm("ew", list(st[[1]]), c("P","MA3"))
  41 #' plotCurves(r)
  42 #' r2 = runAlgorithm("ml", st[c(1,2)], c("MA3","MA10"))
  43 #' plotError(r2)
  44 #' @export
  45 runAlgorithm = function(shortAlgoName, stations, experts, ...)
  46 {
  47     #very basic input checks
  48     if (! shortAlgoName %in% names(algoNameDictionary))
  49         stop("Unknown algorithm:")
  50     experts = unique(experts)
  51
  52     #get data == ordered date indices + forecasts + measures + stations indices (would be DB in prod)
  53     oracleData = getData(stations, experts)
  54
  55     #simulate incremental forecasts acquisition + prediction + get measure
  56     algoData = as.data.frame(matrix(nrow=0, ncol=ncol(oracleData)))
  57     names(algoData) = names(oracleData)
  58     algorithm = new(algoNameDictionary[[shortAlgoName]], data=algoData, ...)
  59     predictions = c()
  60     T = oracleData[nrow(oracleData),"Date"]
  61     for (t in 1:T)
  62     {
  63         #NOTE: bet that subset extract rows in the order they appear
  64         tData = subset(oracleData, subset = (Date==t))
  65         algorithm$inputNextForecasts(tData[,names(tData) != "Measure"])
  66         predictions = c(predictions, algorithm$predict_withNA())
  67         algorithm$inputNextObservations(tData[,"Measure"])
  68     }
  69
  70     oracleData = cbind(oracleData, Prediction = predictions)
  71     return (list(data = oracleData, algo = algorithm, experts = experts, stations = stations))
  72 }