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[aggexp.git] / pkg / R / z_runAlgorithm.R
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1#' @include b_Algorithm.R
2
3algoNameDictionary = 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
45runAlgorithm = 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}