#' \item{\code{getStdHorizon()}}{
#' Number of time steps from serie[1] until midnight}
#' \item{\code{append(time, serie, exo, exo_hat)}}{
-#' Measured data for given vector of times + exogenous predictions from last midgnight.
+#' Measured data for given vector of times + exogenous predictions from
+#' last midgnight.}
#' \item{\code{getTime(index)}}{
#' Times (vector) at specified index.}
#' \item{\code{getCenteredSerie(index)}}{
#'
#' @docType class
#' @format R6 class, inherits Forecaster
-#' @alias F_Average
+#' @aliases F_Average
#'
AverageForecaster = R6::R6Class("AverageForecaster",
inherit = Forecaster,
#'
#' @docType class
#' @format R6 class, inherits Forecaster
-#' @alias F_Neighbors
+#' @aliases F_Neighbors
#'
NeighborsForecaster = R6::R6Class("NeighborsForecaster",
inherit = Forecaster,
return (NA)
# Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays = getNoNA2(data, max(today-memory,1), today-1)
+ fdays = .getNoNA2(data, max(today-memory,1), today-1)
# Get optional args
local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
if (local)
{
- # Neighbors: days in "same season"; TODO: 60 == magic number...
+ # TODO: 60 == magic number
fdays = getSimilarDaysIndices(today, data, limit=60, same_season=TRUE,
days_in=fdays_cut)
if (length(fdays) <= 1)
return (NA)
- levelToday = data$getLevel(today)
- distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
- #TODO: 2, 10, 3, 12 magic numbers here...
- dist_thresh = 2
- min_neighbs = min(10,length(fdays))
- repeat
- {
- same_pollution = (distances <= dist_thresh)
- nb_neighbs = sum(same_pollution)
- if (nb_neighbs >= min_neighbs) #will eventually happen
- break
- dist_thresh = dist_thresh + 3
- }
- fdays = fdays[same_pollution]
- max_neighbs = 12
- if (nb_neighbs > max_neighbs)
- {
- # Keep only max_neighbs closest neighbors
- fdays = fdays[
- sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
- }
- if (length(fdays) == 1) #the other extreme...
+ # TODO: 10, 12 == magic numbers
+ fdays = .getConstrainedNeighbs(today,data,fdays,min_neighbs=10,max_neighbs=12)
+ if (length(fdays) == 1)
{
if (final_call)
{
mean(delta^2)
})
- sd_dist = sd(distances2)
- if (sd_dist < .25 * sqrt(.Machine$double.eps))
- {
-# warning("All computed distances are very close: stdev too small")
- sd_dist = 1 #mostly for tests... FIXME:
- }
- simils_endo = exp(-distances2/(sd_dist*window_endo^2))
+ simils_endo <- .computeSimils(distances2, window_endo)
}
if (simtype == "exo" || simtype == "mix")
delta %*% sigma_inv %*% delta
})
- sd_dist = sd(distances2)
- if (sd_dist < .25 * sqrt(.Machine$double.eps))
- {
-# warning("All computed distances are very close: stdev too small")
- sd_dist = 1 #mostly for tests... FIXME:
- }
- simils_exo = exp(-distances2/(sd_dist*window_exo^2))
+ simils_exo <- .computeSimils(distances2, window_exo)
}
similarities =
}
)
)
+
+#' getNoNA2
+#'
+#' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
+#'
+#' @inheritParams dateIndexToInteger
+#' @param first First index (included)
+#' @param last Last index (included)
+#'
+.getNoNA2 = function(data, first, last)
+{
+ (first:last)[ sapply(first:last, function(i)
+ !any( is.na(data$getCenteredSerie(i)) | is.na(data$getCenteredSerie(i+1)) )
+ ) ]
+}
+
+#' getConstrainedNeighbs
+#'
+#' Get indices of neighbors of similar pollution level (among same season + day type).
+#'
+#' @param today Index of current day
+#' @param data Object of class Data
+#' @param fdays Current set of "first days" (no-NA pairs)
+#' @param min_neighbs Minimum number of points in a neighborhood
+#' @param max_neighbs Maximum number of points in a neighborhood
+#'
+.getConstrainedNeighbs = function(today, data, fdays, min_neighbs=10, max_neighbs=12)
+{
+ levelToday = data$getLevel(today)
+ distances = sapply(fdays, function(i) abs(data$getLevel(i)-levelToday))
+ #TODO: 2, +3 : magic numbers
+ dist_thresh = 2
+ min_neighbs = min(min_neighbs,length(fdays))
+ repeat
+ {
+ same_pollution = (distances <= dist_thresh)
+ nb_neighbs = sum(same_pollution)
+ if (nb_neighbs >= min_neighbs) #will eventually happen
+ break
+ dist_thresh = dist_thresh + 3
+ }
+ fdays = fdays[same_pollution]
+ max_neighbs = 12
+ if (nb_neighbs > max_neighbs)
+ {
+ # Keep only max_neighbs closest neighbors
+ fdays = fdays[
+ sort(distances[same_pollution],index.return=TRUE)$ix[1:max_neighbs] ]
+ }
+ fdsays
+}
+
+#' compute similarities
+#'
+#' Apply the gaussian kernel on computed squared distances.
+#'
+#' @param distances2 Squared distances
+#' @param window Window parameter for the kernel
+#'
+.computeSimils <- function(distances2, window)
+{
+ sd_dist = sd(distances2)
+ if (sd_dist < .25 * sqrt(.Machine$double.eps))
+ {
+# warning("All computed distances are very close: stdev too small")
+ sd_dist = 1 #mostly for tests... FIXME:
+ }
+ exp(-distances2/(sd_dist*window^2))
+}
#'
#' @docType class
#' @format R6 class, inherits Forecaster
-#' @alias F_Persistence
+#' @aliases F_Persistence
#'
PersistenceForecaster = R6::R6Class("PersistenceForecaster",
inherit = Forecaster,
#'
#' @docType class
#' @format R6 class, inherits Forecaster
-#' @alias F_Zero
+#' @aliases F_Zero
#'
ZeroForecaster = R6::R6Class("ZeroForecaster",
inherit = Forecaster,
#' @inheritParams computeForecast
#' @inheritParams getZeroJumpPredict
#'
-#' @alias J_Neighbors
+#' @aliases J_Neighbors
#'
getNeighborsJumpPredict = function(data, today, memory, horizon, params, ...)
{
#' @inheritParams computeForecast
#' @inheritParams getZeroJumpPredict
#'
-#' @alias J_Persistence
+#' @aliases J_Persistence
#'
getPersistenceJumpPredict = function(data, today, memory, horizon, params, ...)
{
#' @param today Index of the current day (predict tomorrow)
#' @param params Optional parameters computed by the main forecaster
#'
-#' @alias J_Zero
+#' @aliases J_Zero
#'
getZeroJumpPredict = function(data, today, memory, horizon, params, ...)
{
#' @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$new()
#' forecaster <- MyForecaster$new(myJumpPredictFunc)
return (day <= 4)
return (day == day_ref)
}
-
-#' getNoNA2
-#'
-#' Get indices in data of no-NA series followed by no-NA, within [first,last] range.
-#'
-#' @inheritParams dateIndexToInteger
-#' @param first First index (included)
-#' @param last Last index (included)
-#'
-#' @export
-getNoNA2 = function(data, first, last)
-{
- (first:last)[ sapply(first:last, function(i)
- !any( is.na(data$getCenteredSerie(i)) | is.na(data$getCenteredSerie(i+1)) )
- ) ]
-}
library(testthat)
-library(talweg)
+
+load_all() #because some non-exported functions
+#library(talweg)
test_check("talweg")
-context("Check that date <--> integer indexes conversions work")
+context("Date <--> integer conversions")
ts_data = system.file("testdata","ts_test.csv",package="talweg")
exo_data = system.file("testdata","exo_test.csv",package="talweg")
data7 <<- getData(ts_data, exo_data, input_tz="GMT", date_format="%Y-%m-%d %H:%M",
working_tz="GMT", predict_at=7, limit=Inf)
-test_that("dateIndexToInteger",
+test_that("dateIndexToInteger works as expected",
{
expect_identical( dateIndexToInteger("2007-01-01",data0), 1 )
expect_identical( dateIndexToInteger("2007-01-02",data0), 2 )
expect_identical( dateIndexToInteger("2007-05-31",data7), 151 )
})
-test_that("integerIndexToDate",
+test_that("integerIndexToDate works as expected",
{
expect_identical( integerIndexToDate( 1,data0), as.Date("2007-01-01") )
expect_identical( integerIndexToDate( 2,data0), as.Date("2007-01-02") )
test_that("integerIndexToDate(dateIndexToInteger) == Id",
{
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-01-01",data0),data0), as.Date("2007-01-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-01-01",data7),data7), as.Date("2007-01-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-01-02",data0),data0), as.Date("2007-01-02") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-01-02",data7),data7), as.Date("2007-01-02") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-02-01",data0),data0), as.Date("2007-02-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-02-01",data0),data0), as.Date("2007-02-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-03-01",data0),data0), as.Date("2007-03-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-03-01",data0),data0), as.Date("2007-03-01") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-05-31",data0),data0), as.Date("2007-05-31") )
- expect_identical(
- integerIndexToDate(dateIndexToInteger("2007-05-31",data0),data0), as.Date("2007-05-31") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-01-01",data0),data0),
+ as.Date("2007-01-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-01-01",data7),data7),
+ as.Date("2007-01-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-01-02",data0),data0),
+ as.Date("2007-01-02") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-01-02",data7),data7),
+ as.Date("2007-01-02") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-02-01",data0),data0),
+ as.Date("2007-02-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-02-01",data0),data0),
+ as.Date("2007-02-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-03-01",data0),data0),
+ as.Date("2007-03-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-03-01",data0),data0),
+ as.Date("2007-03-01") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-05-31",data0),data0),
+ as.Date("2007-05-31") )
+ expect_identical(integerIndexToDate(dateIndexToInteger("2007-05-31",data0),data0),
+ as.Date("2007-05-31") )
})
test_that("Persistence method behave as expected",
{
#Situation A: +Zero; (generally) correct if jump, wrong otherwise
- pred00_sd = computeForecast(data00, indices, "Persistence", "Zero", Inf, 24, same_day=TRUE)
- pred00_dd = computeForecast(data00, indices, "Persistence", "Zero", Inf, 24, same_day=FALSE)
+ pred00_sd = computeForecast(data00, indices, "Persistence", "Zero", Inf, 24,
+ same_day=TRUE)
+ pred00_dd = computeForecast(data00, indices, "Persistence", "Zero", Inf, 24,
+ same_day=FALSE)
for (i in 1:7)
{
expect_equal(pred00_sd$getSerie(i), rep(pred_order[i],24))
expect_equal(pred00_dd$getSerie(i), rep(pred_order[i],24))
}
- pred13_sd = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24, same_day=TRUE)
- pred13_dd = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24, same_day=FALSE)
+ pred13_sd = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24,
+ same_day=TRUE)
+ pred13_dd = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24,
+ same_day=FALSE)
for (i in 2:6)
{
expect_equal(pred13_sd$getSerie(i), c( rep(i,11), rep(i%%7+1,13) ) )
test_that("Neighbors method behave as expected",
{
#Situation A: +Zero; correct if jump, wrong otherwise
- pred00 = computeForecast(data00, indices, "Neighbors", "Zero", Inf, 24, simtype="mix")
+ pred00 = computeForecast(data00, indices, "Neighbors", "Zero", Inf, 24,
+ simtype="mix")
for (i in 1:7)
expect_equal(pred00$getSerie(i), rep(pred_order[i],24))
- pred13 = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24, simtype="mix")
+ pred13 = computeForecast(data13, indices, "Persistence", "Zero", Inf, 24,
+ simtype="mix")
for (i in 1:7)
expect_equal(pred13$getSerie(i), c( rep(i,11), rep(i%%7+1,13) ) )
#Situation B: +Neighbors, always predict bad (small, averaged) jump
- pred00 = computeForecast(data00, indices, "Neighbors", "Neighbors", Inf, 24, simtype="endo")
- #Concerning weights, there are 12+(1 if i>=2) gaps at -6 and 90-12+(i-2 if i>=3) gaps at 1
- #Thus, predicted jump is respectively
+ pred00 = computeForecast(data00, indices, "Neighbors", "Neighbors", Inf, 24,
+ simtype="endo")
+ #Concerning weights, there are 12+(1 if i>=2) gaps at -6 and 90-12+(i-2 if i>=3) gaps
+ #at 1. Thus, predicted jump is respectively
# (12*-6+78)/90 = 0.06666667
# (13*-6+78)/91 = 0
# (13*-6+79)/92 = 0.01086957
for (i in 1:7)
expect_equal(pred00$getSerie(i), rep(pred_order[i]+jumps[i],24))
- #Next lines commented out because too unpredictable results (tendency to flatten everything...)
-# pred13 = computeForecast(data13, indices, "Neighbors", "Neighbors", Inf, 24, simtype="endo")
+ #Next lines commented out because too unpredictable results
+ #(tendency to flatten everything...)
+# pred13 = computeForecast(data13, indices, "Neighbors", "Neighbors", Inf, 24,
+# simtype="endo")
# for (i in 1:7)
# expect_equal(pred13$getSerie(i), c( rep(i,11), rep(i%%7+1,13) ) )
-context("Check that computeFilaments behaves as expected")
+context("computeFilaments")
#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3
I = function(i)
--- /dev/null
+context("Get similar days")
+
+itestthat("getSimilarDaysIndices works as expected",
+{
+ getSimilarDaysIndices(index, data, limit, same_season, days_in=NULL)
+ #...
+})
+{
+ index = dateIndexToInteger(index, data)
+
+testthat("getConstrainedNeighbs works as expected",
+{
+ .getConstrainedNeighbs(today, data, fdays, min_neighbs=10, max_neighbs=12)
+ #...
+})
+