X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2FF_Neighbors.R;h=5b2c8990a850d2dff0d75c23d67815e7393ccd07;hp=7144fad777202020edbd427ea936af940bf34555;hb=5e838b3e17465c376ca075b766cf2543c82e9864;hpb=dea7ff860b42b3e246c8fa7ce2fb514561b8bc43

diff --git a/pkg/R/F_Neighbors.R b/pkg/R/F_Neighbors.R
index 7144fad..5b2c899 100644
--- a/pkg/R/F_Neighbors.R
+++ b/pkg/R/F_Neighbors.R
@@ -1,138 +1,98 @@
 #' @include Forecaster.R
 #'
-#' @title Neighbors Forecaster
+#' Neighbors Forecaster
 #'
-#' @description Predict tomorrow as a weighted combination of "futures of the past" days.
-#'   Inherits \code{\link{Forecaster}}
-NeighborsForecaster = setRefClass(
-	Class = "NeighborsForecaster",
-	contains = "Forecaster",
+#' Predict tomorrow as a weighted combination of "futures of the past" days.
+#' Inherits \code{\link{Forecaster}}
+#'
+NeighborsForecaster = R6::R6Class("NeighborsForecaster",
+	inherit = Forecaster,
 
-	methods = list(
-		initialize = function(...)
-		{
-			callSuper(...)
-		},
-		predictShape = function(today, memory, horizon, ...)
+	public = list(
+		predictShape = function(data, today, memory, horizon, ...)
 		{
 			# (re)initialize computed parameters
-			params <<- list("weights"=NA, "indices"=NA, "window"=NA)
-
-			first_day = max(today - memory, 1)
-			# The first day is generally not complete:
-			if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
-				first_day = 2
+			private$.params <- list("weights"=NA, "indices"=NA, "window"=NA)
 
-			# Predict only on (almost) non-NAs days
-			nas_in_serie = is.na(data$getSerie(today))
-			if (any(nas_in_serie))
-			{
-				#TODO: better define "repairing" conditions (and method)
-				if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
-					return (NA)
-				for (i in seq_along(nas_in_serie))
-				{
-					if (nas_in_serie[i])
-					{
-						#look left
-						left = i-1
-						while (left>=1 && nas_in_serie[left])
-							left = left-1
-						#look right
-						right = i+1
-						while (right<=length(nas_in_serie) && nas_in_serie[right])
-							right = right+1
-						#HACK: modify by-reference Data object...
-						data$data[[today]]$serie[i] <<-
-							if (left==0) data$data[[today]]$serie[right]
-							else if (right==0) data$data[[today]]$serie[left]
-							else (data$data[[today]]$serie[left] + data$data[[today]]$serie[right]) / 2.
-					}
-				}
-			}
+			# Do not forecast on days with NAs (TODO: softer condition...)
+			if (any(is.na(data$getCenteredSerie(today))))
+				return (NA)
 
 			# Determine indices of no-NAs days followed by no-NAs tomorrows
-			fdays_indices = c()
-			for (i in first_day:(today-1))
+			fdays = getNoNA2(data, max(today-memory,1), today-1)
+
+			# Get optional args
+			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "mix") #or "endo", or "exo"
+			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss") #or "Epan"
+			if (hasArg(h_window))
 			{
-				if ( !any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))) )
-					fdays_indices = c(fdays_indices, i)
+				return ( private$.predictShapeAux(data,
+					fdays, today, horizon, list(...)$h_window, kernel, simtype, TRUE) )
 			}
 
-			#GET OPTIONAL PARAMS
-			# Similarity computed with exogenous variables ? endogenous ? both ? ("exo","endo","mix")
-			simtype = ifelse(hasArg("simtype"), list(...)$simtype, "exo")
-			simthresh = ifelse(hasArg("simthresh"), list(...)$simthresh, 0.)
-			kernel = ifelse(hasArg("kernel"), list(...)$kernel, "Gauss")
-			mix_strategy = ifelse(hasArg("mix_strategy"), list(...)$mix_strategy, "neighb") #or "mult"
-			same_season = ifelse(hasArg("same_season"), list(...)$same_season, TRUE)
-			if (hasArg(h_window))
-				return (.predictShapeAux(fdays_indices, today, horizon, list(...)$h_window, kernel,
-					simtype, simthresh, mix_strategy, FALSE))
-			#END GET
+			# Indices of similar days for cross-validation; TODO: 45 = magic number
+			sdays = getSimilarDaysIndices(today, limit=45, same_season=FALSE)
 
-			# Indices for cross-validation; TODO: 45 = magic number
-			indices = getSimilarDaysIndices(today, limit=45, same_season=same_season)
-			#indices = (end_index-45):(end_index-1)
+			cv_days = intersect(fdays,sdays)
+			# Limit to 20 most recent matching days (TODO: 20 == magic number)
+			cv_days = sort(cv_days,decreasing=TRUE)[1:min(20,length(cv_days))]
 
 			# Function to optimize h : h |--> sum of prediction errors on last 45 "similar" days
 			errorOnLastNdays = function(h, kernel, simtype)
 			{
 				error = 0
 				nb_jours = 0
-				for (i in indices)
+				for (i in seq_along(cv_days))
 				{
-					# NOTE: predict only on non-NAs days followed by non-NAs (TODO:)
-					if (!any(is.na(data$getSerie(i)) | is.na(data$getSerie(i+1))))
+					# mix_strategy is never used here (simtype != "mix"), therefore left blank
+					prediction = private$.predictShapeAux(data,
+						fdays, cv_days[i], horizon, h, kernel, simtype, FALSE)
+					if (!is.na(prediction[1]))
 					{
 						nb_jours = nb_jours + 1
-						# mix_strategy is never used here (simtype != "mix"), therefore left blank
-						prediction = .predictShapeAux(fdays_indices, i, horizon, h, kernel, simtype,
-							simthresh, "", FALSE)
-						if (!is.na(prediction[1]))
-							error = error + mean((data$getCenteredSerie(i+1)[1:horizon] - prediction)^2)
+						error = error +
+							mean((data$getCenteredSerie(cv_days[i]+1)[1:horizon] - prediction)^2)
 					}
 				}
 				return (error / nb_jours)
 			}
 
-			h_best_exo = 1.
-			if (simtype != "endo" && !(simtype=="mix" && mix_strategy=="neighb"))
+			if (simtype != "endo")
 			{
-				h_best_exo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
-					simtype="exo")$minimum
+				h_best_exo = optimize(
+					errorOnLastNdays, c(0,10), kernel=kernel, simtype="exo")$minimum
 			}
 			if (simtype != "exo")
 			{
-				h_best_endo = optimize(errorOnLastNdays, interval=c(0,10), kernel=kernel,
-					simtype="endo")$minimum
+				h_best_endo = optimize(
+					errorOnLastNdays, c(0,10), kernel=kernel, simtype="endo")$minimum
 			}
 
 			if (simtype == "endo")
 			{
-				return (.predictShapeAux(fdays_indices, today, horizon, h_best_endo, kernel, "endo",
-					simthresh, "", TRUE))
+				return (private$.predictShapeAux(data,
+					fdays, today, horizon, h_best_endo, kernel, "endo", TRUE))
 			}
 			if (simtype == "exo")
 			{
-				return (.predictShapeAux(fdays_indices, today, horizon, h_best_exo, kernel, "exo",
-					simthresh, "", TRUE))
+				return (private$.predictShapeAux(data,
+					fdays, today, horizon, h_best_exo, kernel, "exo", TRUE))
 			}
 			if (simtype == "mix")
 			{
-				return (.predictShapeAux(fdays_indices, today, horizon, c(h_best_endo,h_best_exo),
-					kernel, "mix", simthresh, mix_strategy, TRUE))
+				h_best_mix = c(h_best_endo,h_best_exo)
+				return(private$.predictShapeAux(data,
+					fdays, today, horizon, h_best_mix, kernel, "mix", TRUE))
 			}
-		},
+		}
+	),
+	private = list(
 		# Precondition: "today" is full (no NAs)
-		.predictShapeAux = function(fdays_indices, today, horizon, h, kernel, simtype, simthresh,
-			mix_strategy, final_call)
+		.predictShapeAux = function(data, fdays, today, horizon, h, kernel, simtype, final_call)
 		{
-			dat = data$data #HACK: faster this way...
-
-			fdays_indices = fdays_indices[fdays_indices < today]
+			fdays = fdays[ fdays < today ]
 			# TODO: 3 = magic number
-			if (length(fdays_indices) < 3)
+			if (length(fdays) < 3)
 				return (NA)
 
 			if (simtype != "exo")
@@ -140,20 +100,24 @@ NeighborsForecaster = setRefClass(
 				h_endo = ifelse(simtype=="mix", h[1], h)
 
 				# Distances from last observed day to days in the past
-				distances2 = rep(NA, length(fdays_indices))
-				for (i in seq_along(fdays_indices))
-				{
-					delta = dat[[today]]$serie - dat[[ fdays_indices[i] ]]$serie
-					# Require at least half of non-NA common values to compute the distance
-					if (sum(is.na(delta)) <= 0) #length(delta)/2)
-						distances2[i] = mean(delta^2) #, na.rm=TRUE)
-				}
+				serieToday = data$getSerie(today)
+				distances2 = sapply(fdays, function(i) {
+					delta = serieToday - data$getSerie(i)
+					mean(delta^2)
+				})
 
 				sd_dist = sd(distances2)
+				if (sd_dist < .Machine$double.eps)
+				{
+#					warning("All computed distances are very close: stdev too small")
+					sd_dist = 1 #mostly for tests... FIXME:
+				}
 				simils_endo =
-					if (kernel=="Gauss") {
+					if (kernel=="Gauss")
 						exp(-distances2/(sd_dist*h_endo^2))
-					} else { #Epanechnikov
+					else
+					{
+						# Epanechnikov
 						u = 1 - distances2/(sd_dist*h_endo^2)
 						u[abs(u)>1] = 0.
 						u
@@ -164,92 +128,64 @@ NeighborsForecaster = setRefClass(
 			{
 				h_exo = ifelse(simtype=="mix", h[2], h)
 
-				M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo) )
-				M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
-				for (i in seq_along(fdays_indices))
-				{
-					M[i+1,] = c( dat[[ fdays_indices[i] ]]$level,
-						as.double(dat[[ fdays_indices[i] ]]$exo) )
-				}
+				M = matrix( nrow=1+length(fdays), ncol=1+length(data$getExo(today)) )
+				M[1,] = c( data$getLevel(today), as.double(data$getExo(today)) )
+				for (i in seq_along(fdays))
+					M[i+1,] = c( data$getLevel(fdays[i]), as.double(data$getExo(fdays[i])) )
 
 				sigma = cov(M) #NOTE: robust covariance is way too slow
-				sigma_inv = qr.solve(sigma)
+				sigma_inv = solve(sigma) #TODO: use pseudo-inverse if needed?
 
 				# Distances from last observed day to days in the past
-				distances2 = rep(NA, nrow(M)-1)
-				for (i in 2:nrow(M))
-				{
-					delta = M[1,] - M[i,]
-					distances2[i-1] = delta %*% sigma_inv %*% delta
-				}
+				distances2 = sapply(seq_along(fdays), function(i) {
+					delta = M[1,] - M[i+1,]
+					delta %*% sigma_inv %*% delta
+				})
 
 				sd_dist = sd(distances2)
+				if (sd_dist < .Machine$double.eps)
+				{
+#					warning("All computed distances are very close: stdev too small")
+					sd_dist = 1 #mostly for tests... FIXME:
+				}
 				simils_exo =
-					if (kernel=="Gauss") {
+					if (kernel=="Gauss")
 						exp(-distances2/(sd_dist*h_exo^2))
-					} else { #Epanechnikov
+					else
+					{
+						# Epanechnikov
 						u = 1 - distances2/(sd_dist*h_exo^2)
 						u[abs(u)>1] = 0.
 						u
 					}
 			}
 
-			if (simtype=="mix")
-			{
-				if (mix_strategy == "neighb")
-				{
-					#Only (60) most similar days according to exogen variables are kept into consideration
-					#TODO: 60 = magic number
-					keep_indices = sort(simils_exo, index.return=TRUE)$ix[1:(min(60,length(simils_exo)))]
-					simils_endo[-keep_indices] = 0.
-				} else #mix_strategy == "mult"
-				{
-					simils_endo = simils_endo * simils_exo
-				}
-			}
-
 			similarities =
-				if (simtype != "exo") {
-					simils_endo
-				} else {
+				if (simtype == "exo")
 					simils_exo
-				}
-
-			if (simthresh > 0.)
-			{
-				max_sim = max(similarities)
-				# Set to 0 all similarities s where s / max_sim < simthresh, but keep at least 60
-				ordering = sort(similarities / max_sim, index.return=TRUE)
-				if (ordering[60] < simthresh)
-				{
-					similarities[ ordering$ix[ - (1:60) ] ] = 0.
-				} else
-				{
-					limit = 61
-					while (limit < length(similarities) && ordering[limit] >= simthresh)
-						limit = limit + 1
-					similarities[ ordering$ix[ - 1:limit] ] = 0.
-				}
-			}
+				else if (simtype == "endo")
+					simils_endo
+				else #mix
+					simils_endo * simils_exo
 
 			prediction = rep(0, horizon)
-			for (i in seq_along(fdays_indices))
-				prediction = prediction + similarities[i] * dat[[ fdays_indices[i]+1 ]]$serie[1:horizon]
-
+			for (i in seq_along(fdays))
+				prediction = prediction + similarities[i] * data$getCenteredSerie(fdays[i]+1)[1:horizon]
 			prediction = prediction / sum(similarities, na.rm=TRUE)
+
 			if (final_call)
 			{
-				params$weights <<- similarities
-				params$indices <<- fdays_indices
-				params$window <<-
-					if (simtype=="endo") {
+				private$.params$weights <- similarities
+				private$.params$indices <- fdays
+				private$.params$window <-
+					if (simtype=="endo")
 						h_endo
-					} else if (simtype=="exo") {
+					else if (simtype=="exo")
 						h_exo
-					} else {
+					else #mix
 						c(h_endo,h_exo)
-					}
 			}
+
 			return (prediction)
 		}
 	)