X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeForecast.R;h=ef46dd3b7f516c23ef91430a923652224c924e1f;hb=638f27f4296727aff62b56643beb9f42aa5b57ef;hp=198f6ec1bc03919d773a5e45c045c876a9e4d72a;hpb=72b9c50162bcdcf6c99fbb8b2ec6ea9ba98379cb;p=talweg.git

diff --git a/pkg/R/computeForecast.R b/pkg/R/computeForecast.R
index 198f6ec..ef46dd3 100644
--- a/pkg/R/computeForecast.R
+++ b/pkg/R/computeForecast.R
@@ -1,51 +1,66 @@
 #' 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 Dataset, object of type \code{Data} output of \code{getData}
-#' @param indices Days indices where to forecast (the day after)
-#' @param forecaster Name of the main forcaster
+#' @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>
 #' \itemize{
-#'   \item Persistence : use values of last (similar, next) day
-#'   \item Neighbors : use values from the k closest neighbors' tomorrows
-#'   \item Average : global average of all the (similar) "tomorrow of past"
+#'   \item Persistence : use last (similar, next) day
+#'   \item Neighbors : weighted tomorrows of similar days
+#'   \item Average : average tomorrow of all same day-in-week
 #'   \item Zero : just output 0 (benchmarking purpose)
 #' }
-#' @param pjump How to predict the jump at the interface between two days ?
+#' @param pjump Function to predict the jump at the interface between two days;
+#'   more details: ?J_<functionname>
 #' \itemize{
-#'   \item Persistence : use last (similar) day values
-#'   \item Neighbors: re-use the weights optimized in corresponding forecaster
+#'   \item Persistence : use last (similar, next) day
+#'   \item Neighbors: re-use the weights from F_Neighbors
 #'   \item Zero: just output 0 (no adjustment)
 #' }
-#' @param memory Data depth (in days) to be used for prediction
-#' @param horizon Number of time steps to predict
-#' @param ncores Number of cores for parallel execution (1 to disable)
-#' @param ... Additional parameters for the forecasting models;
-#'   In particular, realtime=TRUE to use predictions instead of measurements
+#' @param memory Data depth (in days) to be used for prediction.
+#' @param horizon Number of time steps to predict.
+#' @param ncores Number of cores for parallel execution (1 to disable).
+#' @param ... Additional parameters for the forecasting models.
 #'
 #' @return An object of class Forecast
 #'
 #' @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", "Persistence", 500, 12)
+#' 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, limit=200)
+#' pred <- computeForecast(data, 100:130, "Persistence", "Zero",
+#'   memory=50, horizon=12, ncores=1)
 #' \dontrun{#Sketch for real-time mode:
-#' data = new("Data", ...)
-#' forecaster = new(..., data=data)
+#' data <- Data$new()
+#' forecaster <- MyForecaster$new(myJumpPredictFunc)
 #' repeat {
-#'   data$append(some_new_data)
-#'   pred = forecaster$predict(data$getSize(), ...)
+#'   # In the morning 7am+ or afternoon 1pm+:
+#'   data$append(
+#'     times_from_H+1_yersteday_to_Hnow,
+#'     PM10_values_of_last_24h,
+#'     exogenous_measures_of_last_24h,
+#'     exogenous_predictions_for_next_24h)
+#'   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$getSize()))
 		stop("Indices out of range")
@@ -58,27 +73,20 @@ computeForecast = function(data, indices, forecaster, pjump,
 	forecaster = forecaster_class_name$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))
 	{