adapt Bruno method into package, add 'operational' mode

[talweg.git] / pkg / R / computeForecast.R

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 53dbc14..ef46dd3 100644 (file)
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,8 +1,10 @@
 #' Compute forecast
 #'
-#' Predict time-series curves for the selected days indices (lines in data).
+#' Predict time-series curves ("tomorrows") at the selected days indices ("todays").
+#' This function just runs a loop over all requested indices, and stores the individual
+#' forecasts into a list which is then turned into a Forecast object.
 #'
-#' @param data Object of type \code{Data}, output of \code{getData()}.
+#' @param data Object of class Data, output of \code{getData()}.
 #' @param indices Indices where to forecast (the day after); integers relative to the
 #'   beginning of data, or (convertible to) Date objects.
 #' @param forecaster Name of the main forecaster; more details: ?F_<forecastername>
@@ -29,13 +31,12 @@
 #' @examples
 #' ts_data <- system.file("extdata","pm10_mesures_H_loc.csv",package="talweg")
 #' exo_data <- system.file("extdata","meteo_extra_noNAs.csv",package="talweg")
-#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT", predict_at=7)
-#' pred <- computeForecast(data, 2200:2230, "Persistence", "Zero",
-#'   memory=500, horizon=12, ncores=1)
+#' data <- getData(ts_data, exo_data, input_tz="GMT", working_tz="GMT",
+#'   predict_at=7, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#'   memory=50, horizon=12, ncores=1)
 #' \dontrun{#Sketch for real-time mode:
 #' data <- Data$new()
-#' # Initialize: first day has no predictions attached
-#' data$initialize()
 #' forecaster <- MyForecaster$new(myJumpPredictFunc)
 #' repeat {
 #'   # In the morning 7am+ or afternoon 1pm+:
@@ -44,17 +45,22 @@
 #'     PM10_values_of_last_24h,
 #'     exogenous_measures_of_last_24h,
 #'     exogenous_predictions_for_next_24h)
-#'   pred <- forecaster$predictSerie(data, data$getSize()-1, ...)
+#'   pred <- forecaster$predictSerie(data, data$getSize(), ...)
 #'   #do_something_with_pred
 #' }}
 #' @export
-computeForecast = function(data, indices, forecaster, pjump,
-       memory=Inf, horizon=data$getStdHorizon(), ncores=3, ...)
+computeForecast = function(data, indices, forecaster, pjump, predict_from,
+       memory=Inf, horizon=length(data$getSerie(1)), ncores=3, ...)
 {
        # (basic) Arguments sanity checks
+       predict_from = as.integer(predict_from)[1]
+       if (! predict_from %in% 1:length(data$getSerie(1)))
+               stop("predict_from in [1,24] (hours)")
+       if (hasArg("opera") && !list(...)$opera && memory < Inf)
+               memory <- Inf #finite memory in training mode makes no sense
        horizon = as.integer(horizon)[1]
-       if (horizon<=0 || horizon>length(data$getCenteredSerie(1)))
-               stop("Horizon too short or too long")
+       if (horizon<=predict_from || horizon>length(data$getSerie(1)))
+               stop("Horizon in [predict_from+1,24] (hours)")
        integer_indices = sapply(indices, function(i) dateIndexToInteger(i,data))
        if (any(integer_indices<=0 | integer_indices>data$getSize()))
                stop("Indices out of range")
@@ -67,27 +73,20 @@ computeForecast = function(data, indices, forecaster, pjump,
        forecaster = forecaster_class_name$new( #.pjump =
                getFromNamespace(paste("get",pjump,"JumpPredict",sep=""), "talweg"))
 
-       if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+       computeOneForecast <- function(i)
        {
-               p <- parallel::mclapply(seq_along(integer_indices), function(i) {
-                       list(
-                               "forecast" = forecaster$predictSerie(
-                                       data, integer_indices[i], memory, horizon, ...),
-                               "params"= forecaster$getParameters(),
-                               "index" = integer_indices[i] )
-                       }, mc.cores=ncores)
-       }
-       else
-       {
-               p <- lapply(seq_along(integer_indices), function(i) {
-                       list(
-                               "forecast" = forecaster$predictSerie(
-                                       data, integer_indices[i], memory, horizon, ...),
-                               "params"= forecaster$getParameters(),
-                               "index" = integer_indices[i] )
-                       })
+               list(
+                       "forecast" = forecaster$predictSerie(data,i,memory,predict_from,horizon,...),
+                       "params" = forecaster$getParameters(),
+                       "index" = i )
        }
 
+       p <-
+               if (ncores > 1 && requireNamespace("parallel",quietly=TRUE))
+                       parallel::mclapply(integer_indices, computeOneForecast, mc.cores=ncores)
+               else
+                       lapply(integer_indices, computeOneForecast)
+
        # TODO: find a way to fill pred in //...
        for (i in seq_along(integer_indices))
        {