'update'
[talweg.git] / pkg / R / F_Neighbors.R
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25b75559 1#' Neighbors Forecaster
3d69ff21 2#'
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3#' Predict next serie as a weighted combination of curves observed on "similar" days in
4#' the past (and future if 'opera'=FALSE); the nature of the similarity is controlled by
5#' the options 'simtype' and 'local' (see below).
c4c329f6 6#'
4f3fdbb8 7#' Optional arguments:
102bcfda 8#' \itemize{
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9#' \item local: TRUE (default) to constrain neighbors to be "same days in same season"
10#' \item simtype: 'endo' for a similarity based on the series only,<cr>
d2ab47a7 11#' 'exo' for a similarity based on exogenous variables only,<cr>
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12#' 'mix' for the product of 'endo' and 'exo',<cr>
13#' 'none' (default) to apply a simple average: no computed weights
4f3fdbb8 14#' \item window: A window for similarities computations; override cross-validation
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15#' window estimation.
16#' }
17#' The method is summarized as follows:
18#' \enumerate{
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19#' \item Determine N (=20) recent days without missing values, and preceded by a
20#' curve also without missing values.
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21#' \item Optimize the window parameters (if relevant) on the N chosen days.
22#' \item Considering the optimized window, compute the neighbors (with locality
23#' constraint or not), compute their similarities -- using a gaussian kernel if
24#' simtype != "none" -- and average accordingly the "tomorrows of neigbors" to
25#' obtain the final prediction.
26#' }
c4c329f6 27#'
4e821712 28#' @usage # NeighborsForecaster$new(pjump)
689aa1d3 29#'
102bcfda 30#' @docType class
c4c329f6 31#' @format R6 class, inherits Forecaster
3ddf1c12 32#' @aliases F_Neighbors
546b0cb6 33#'
25b75559 34NeighborsForecaster = R6::R6Class("NeighborsForecaster",
a66a84b5 35 inherit = Forecaster,
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36
37 public = list(
d2ab47a7 38 predictShape = function(data, today, memory, predict_from, horizon, ...)
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39 {
40 # (re)initialize computed parameters
a66a84b5 41 private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
3d69ff21 42
a5a3a294 43 # Do not forecast on days with NAs (TODO: softer condition...)
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44 if (any(is.na(data$getSerie(today-1))) ||
45 (predict_from>=2 && any(is.na(data$getSerie(today)[1:(predict_from-1)]))))
d2ab47a7 46 {
a5a3a294 47 return (NA)
d2ab47a7 48 }
a5a3a294 49
f17665c7 50 # Get optional args
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51 local = ifelse(hasArg("local"), list(...)$local, TRUE) #same level + season?
52 simtype = ifelse(hasArg("simtype"), list(...)$simtype, "none") #or "endo", or "exo"
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53 opera = ifelse(hasArg("opera"), list(...)$opera, FALSE) #operational mode?
54
55 # Determine indices of no-NAs days preceded by no-NAs yerstedays
56 tdays = .getNoNA2(data, max(today-memory,2), ifelse(opera,today-1,data$getSize()))
57 if (!opera)
58 tdays = setdiff(tdays, today) #always exclude current day
59
8f5671db 60 # Shortcut if window is known
7c4b2952 61 if (hasArg("window"))
a66a84b5 62 {
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63 return ( private$.predictShapeAux(data, tdays, today, predict_from, horizon,
64 local, list(...)$window, simtype, opera, TRUE) )
a66a84b5 65 }
3d69ff21 66
6774e53d 67 # Indices of similar days for cross-validation; TODO: 20 = magic number
aa059de7 68 cv_days = getSimilarDaysIndices(today, data, limit=20, same_season=FALSE,
638f27f4 69 days_in=tdays, operational=opera)
5e838b3e 70
445e7bbc 71 # Optimize h : h |--> sum of prediction errors on last N "similar" days
aa059de7 72 errorOnLastNdays = function(window, simtype)
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73 {
74 error = 0
75 nb_jours = 0
5e838b3e 76 for (i in seq_along(cv_days))
3d69ff21 77 {
f17665c7 78 # mix_strategy is never used here (simtype != "mix"), therefore left blank
cf3bb001 79 prediction = private$.predictShapeAux(data, tdays, cv_days[i], predict_from,
638f27f4 80 horizon, local, window, simtype, opera, FALSE)
f17665c7 81 if (!is.na(prediction[1]))
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82 {
83 nb_jours = nb_jours + 1
af3b84f4 84 error = error +
cf3bb001 85 mean((data$getSerie(cv_days[i])[predict_from:horizon] - prediction)^2)
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86 }
87 }
88 return (error / nb_jours)
89 }
90
445e7bbc 91 # TODO: 7 == magic number
eef54517 92 if (simtype=="endo" || simtype=="mix")
af3b84f4 93 {
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94 best_window_endo = optimize(
95 errorOnLastNdays, c(0,7), simtype="endo")$minimum
af3b84f4 96 }
eef54517 97 if (simtype=="exo" || simtype=="mix")
af3b84f4 98 {
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99 best_window_exo = optimize(
100 errorOnLastNdays, c(0,7), simtype="exo")$minimum
3d69ff21 101 }
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102 if (local)
103 {
104 best_window_local = optimize(
105 errorOnLastNdays, c(3,30), simtype="none")$minimum
106 }
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107
108 best_window =
109 if (simtype == "endo")
110 best_window_endo
111 else if (simtype == "exo")
112 best_window_exo
113 else if (simtype == "mix")
114 c(best_window_endo,best_window_exo)
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115 else #none: no value
116 NULL
117 if (local)
118 best_window = c(best_window, best_window_local)
eef54517 119
cf3bb001 120 return( private$.predictShapeAux(data, tdays, today, predict_from, horizon, local,
638f27f4 121 best_window, simtype, opera, TRUE) )
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122 }
123 ),
124 private = list(
638f27f4 125 # Precondition: "yersteday until predict_from-1" is full (no NAs)
cf3bb001 126 .predictShapeAux = function(data, tdays, today, predict_from, horizon, local, window,
638f27f4 127 simtype, opera, final_call)
3d69ff21 128 {
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129 tdays_cut = tdays[ tdays != today ]
130 if (length(tdays_cut) == 0)
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131 return (NA)
132
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133 if (local)
134 {
dca259e4 135 # limit=Inf to not censor any day (TODO: finite limit? 60?)
8f5671db 136 tdays <- getSimilarDaysIndices(today, data, limit=Inf, same_season=TRUE,
638f27f4 137 days_in=tdays_cut, operational=opera)
8f5671db 138 nb_neighbs <- round( window[length(window)] )
638f27f4 139 # TODO: 10 == magic number
a3344f75 140 tdays <- .getConstrainedNeighbs(today, data, tdays, nb_neighbs, opera)
cf3bb001 141 if (length(tdays) == 1)
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142 {
143 if (final_call)
144 {
145 private$.params$weights <- 1
cf3bb001 146 private$.params$indices <- tdays
8f5671db 147 private$.params$window <- window
aa059de7 148 }
cf3bb001 149 return ( data$getSerie(tdays[1])[predict_from:horizon] )
aa059de7 150 }
8f5671db 151 max_neighbs = nb_neighbs #TODO: something else?
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152 if (length(tdays) > max_neighbs)
153 {
154 distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
155 ordering <- order(distances2)
156 tdays <- tdays[ ordering[1:max_neighbs] ]
157 }
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158 }
159 else
cf3bb001 160 tdays = tdays_cut #no conditioning
aa059de7 161
445e7bbc 162 if (simtype == "endo" || simtype == "mix")
3d69ff21 163 {
638f27f4 164 # Distances from last observed day to selected days in the past
dca259e4 165 # TODO: redundant computation if local==TRUE
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166 distances2 <- .computeDistsEndo(data, today, tdays, predict_from)
167
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168 # Compute endogen similarities using the given window
169 simils_endo <- .computeSimils(distances2, window[1])
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170 }
171
445e7bbc 172 if (simtype == "exo" || simtype == "mix")
3d69ff21 173 {
a3344f75 174 distances2 <- .computeDistsExo(data, today, tdays, opera)
3d69ff21 175
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176 # Compute exogen similarities using the given window
177 window_exo = ifelse(simtype=="mix", window[2], window[1])
3ddf1c12 178 simils_exo <- .computeSimils(distances2, window_exo)
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179 }
180
3d69ff21 181 similarities =
f17665c7 182 if (simtype == "exo")
3d69ff21 183 simils_exo
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184 else if (simtype == "endo")
185 simils_endo
445e7bbc 186 else if (simtype == "mix")
f17665c7 187 simils_endo * simils_exo
445e7bbc 188 else #none
cf3bb001 189 rep(1, length(tdays))
ea5c7e56 190 similarities = similarities / sum(similarities)
3d69ff21 191
d2ab47a7 192 prediction = rep(0, horizon-predict_from+1)
cf3bb001 193 for (i in seq_along(tdays))
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194 {
195 prediction = prediction +
cf3bb001 196 similarities[i] * data$getSerie(tdays[i])[predict_from:horizon]
d2ab47a7 197 }
99f83c9a 198
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199 if (final_call)
200 {
a66a84b5 201 private$.params$weights <- similarities
cf3bb001 202 private$.params$indices <- tdays
8f5671db 203 private$.params$window <- window
3d69ff21 204 }
99f83c9a 205
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206 return (prediction)
207 }
208 )
209)
3ddf1c12 210
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211# getConstrainedNeighbs
212#
213# Get indices of neighbors of similar pollution level (among same season + day type).
214#
215# @param today Index of current day
216# @param data Object of class Data
cf3bb001 217# @param tdays Current set of "second days" (no-NA pairs)
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218# @param min_neighbs Minimum number of points in a neighborhood
219# @param max_neighbs Maximum number of points in a neighborhood
220#
a3344f75 221.getConstrainedNeighbs = function(today, data, tdays, min_neighbs, opera)
3ddf1c12 222{
a3344f75 223 levelToday = ifelse(opera, tail(data$getLevelHat(today),1), data$getLevel(today))
9b9bb2d4 224 distances = sapply( tdays, function(i) abs(data$getLevel(i) - levelToday) )
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225 #TODO: 1, +1, +3 : magic numbers
226 dist_thresh = 1
cf3bb001 227 min_neighbs = min(min_neighbs,length(tdays))
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228 repeat
229 {
230 same_pollution = (distances <= dist_thresh)
231 nb_neighbs = sum(same_pollution)
232 if (nb_neighbs >= min_neighbs) #will eventually happen
233 break
d2ab47a7 234 dist_thresh = dist_thresh + ifelse(dist_thresh>1,3,1)
3ddf1c12 235 }
9b9bb2d4 236 tdays[same_pollution]
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237}
238
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239# compute similarities
240#
241# Apply the gaussian kernel on computed squared distances.
242#
243# @param distances2 Squared distances
244# @param window Window parameter for the kernel
245#
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246.computeSimils <- function(distances2, window)
247{
248 sd_dist = sd(distances2)
249 if (sd_dist < .25 * sqrt(.Machine$double.eps))
250 {
251# warning("All computed distances are very close: stdev too small")
252 sd_dist = 1 #mostly for tests... FIXME:
253 }
254 exp(-distances2/(sd_dist*window^2))
255}
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256
257.computeDistsEndo <- function(data, today, tdays, predict_from)
258{
259 lastSerie = c( data$getSerie(today-1),
260 data$getSerie(today)[if (predict_from>=2) 1:(predict_from-1) else c()] )
261 sapply(tdays, function(i) {
262 delta = lastSerie - c(data$getSerie(i-1),
263 data$getSerie(i)[if (predict_from>=2) 1:(predict_from-1) else c()])
9b9bb2d4 264 sqrt(mean(delta^2))
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265 })
266}
267
a3344f75 268.computeDistsExo <- function(data, today, tdays, opera)
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269{
270 M = matrix( ncol=1+length(tdays), nrow=1+length(data$getExo(1)) )
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271 if (opera)
272 M[,1] = c( tail(data$getLevelHat(today),1), as.double(data$getExoHat(today)) )
273 else
274 M[,1] = c( data$getLevel(today), as.double(data$getExo(today)) )
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275 for (i in seq_along(tdays))
276 M[,i+1] = c( data$getLevel(tdays[i]), as.double(data$getExo(tdays[i])) )
277
278 sigma = cov(t(M)) #NOTE: robust covariance is way too slow
279 # TODO: 10 == magic number; more robust way == det, or always ginv()
280 sigma_inv =
281 if (length(tdays) > 10)
282 solve(sigma)
283 else
284 MASS::ginv(sigma)
285
286 # Distances from last observed day to days in the past
287 sapply(seq_along(tdays), function(i) {
288 delta = M[,1] - M[,i+1]
289 delta %*% sigma_inv %*% delta
290 })
291}