#' Plot curves
#'
-#' Plot a range of curves in data
+#' Plot a range of curves in data.
#'
-#' @param data Object of class Data
+#' @inheritParams computeError
#' @param indices Range of indices (integers or dates)
#'
#' @export
plotCurves <- function(data, indices=seq_len(data$getSize()))
{
series = data$getSeries(indices)
- yrange = quantile(series, probs=c(0.05,0.95), na.rm=TRUE)
+ yrange = quantile(series, probs=c(0.025,0.975), na.rm=TRUE)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- for (i in seq_along(indices))
- {
- plot(series[,i], type="l", ylim=yrange,
- xlab=ifelse(i==1,"Temps (en heures)",""), ylab=ifelse(i==1,"PM10",""))
- if (i < length(indices))
- par(new=TRUE)
- }
+ matplot(series, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
}
#' Plot error
#'
-#' Draw error graphs, potentially from several runs of \code{computeForecast}
+#' Draw error graphs, potentially from several runs of \code{computeForecast()}.
#'
-#' @param err Error as returned by \code{computeError}
+#' @param err Error as returned by \code{computeError()}
#' @param cols Colors for each error (default: 1,2,3,...)
+#' @param agg Aggregation level ("day", "week" or "month")
#'
#' @seealso \code{\link{plotCurves}}, \code{\link{plotPredReal}},
-#' \code{\link{plotSimils}}, \code{\link{plotFbox}},
-#' \code{\link{computeFilaments}, }\code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
+#' \code{\link{plotSimils}}, \code{\link{plotFbox}}, \code{\link{computeFilaments}},
+#' \code{\link{plotFilamentsBox}}, \code{\link{plotRelVar}}
#'
#' @export
-plotError <- function(err, cols=seq_along(err))
+plotError <- function(err, cols=seq_along(err), agg="day")
{
if (!is.null(err$abs))
err = list(err)
- par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
+ par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
L = length(err)
- yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$day ) ), na.rm=TRUE )
- for (i in seq_len(L))
- {
- plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
- ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
- if (i < L)
- par(new=TRUE)
- }
- yrange = range( sapply(1:L, function(i) ( err[[i]]$abs$indices ) ), na.rm=TRUE )
- for (i in seq_len(L))
- {
- plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
- ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
- if (i < L)
- par(new=TRUE)
- }
- yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$day ) ), na.rm=TRUE )
- for (i in seq_len(L))
- {
- plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
- ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
- if (i < L)
- par(new=TRUE)
- }
- yrange = range( sapply(1:L, function(i) ( err[[i]]$MAPE$indices ) ), na.rm=TRUE )
- for (i in seq_len(L))
- {
- plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
- ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
- if (i < L)
- par(new=TRUE)
- }
+
+ yrange = range( sapply(1:L, function(i) err[[i]]$abs$day), na.rm=TRUE )
+ matplot(sapply( seq_len(L), function(i) err[[i]]$abs$day ), type="l",
+ xlab="Time (hours)", ylab="Mean |y - y_hat|", ylim=yrange, col=cols, lwd=2, lty=1)
+
+ agg_curves <- sapply( seq_len(L), function(i) {
+ curve <- err[[i]]$abs$indices
+ delta <- if (agg=="day") 1 else if (agg=="week") 7 else if (agg=="month") 30
+ vapply( seq(1,length(curve),delta), function(i) {
+ mean(curve[i:(i+delta-1)], na.rm=TRUE)
+ }, vector("double",1), USE.NAMES=FALSE )
+ })
+ yrange = range(agg_curves, na.rm=TRUE)
+ matplot(agg_curves, type="l", xlab=paste("Time (",agg,"s)", sep=""),
+ ylab="Mean |y - y_hat|", ylim=yrange, col=cols, lwd=2, lty=1)
+
+ yrange = range( sapply(1:L, function(i) err[[i]]$MAPE$day), na.rm=TRUE )
+ matplot(sapply( seq_len(L), function(i) err[[i]]$MAPE$day ), type="l",
+ xlab="Time (hours)", ylab="Mean MAPE", ylim=yrange, col=cols, lwd=2, lty=1)
+
+ agg_curves <- sapply( seq_len(L), function(i) {
+ curve <- err[[i]]$MAPE$indices
+ delta <- if (agg=="day") 1 else if (agg=="week") 7 else if (agg=="month") 30
+ vapply( seq(1,length(curve),delta), function(i) {
+ mean(curve[i:(i+delta-1)], na.rm=TRUE)
+ }, vector("double",1), USE.NAMES=FALSE )
+ })
+ yrange = range(agg_curves, na.rm=TRUE)
+ matplot(agg_curves, type="l", xlab=paste("Time (",agg,"s)", sep=""),
+ ylab="Mean MAPE", ylim=yrange, col=cols, lwd=2, lty=1)
}
#' Plot measured / predicted
#'
-#' Plot measured curve (in black) and predicted curve (in blue)
+#' Plot measured curve (in black) and predicted curve (in blue).
#'
-#' @param data Object return by \code{getData}
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
plotPredReal <- function(data, pred, index)
{
- horizon = length(pred$getSerie(1))
- measure = data$getSerie( pred$getIndexInData(index)+1 )[1:horizon]
- prediction = pred$getSerie(index)
+ prediction = pred$getForecast(index)
+ measure = data$getSerie( pred$getIndexInData(index) )[1:length(pred$getForecast(1))]
+
+ # Remove the common part, where prediction == measure
+ dot_mark <- ifelse(prediction[1]==measure[1],
+ which.max(seq_along(prediction)[prediction==measure]), 0)
+ prediction = prediction[(dot_mark+1):length(prediction)]
+ measure = measure[(dot_mark+1):length(measure)]
+
yrange = range(measure, prediction)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=3)
- plot(measure, type="l", ylim=yrange, xlab="Temps (en heures)", ylab="PM10")
+ plot(measure, type="l", ylim=yrange, xlab="Time (hours)", ylab="PM10")
par(new=TRUE)
plot(prediction, type="l", col="#0000FF", ylim=yrange, xlab="", ylab="")
}
#' Plot similarities
#'
-#' Plot histogram of similarities (weights)
+#' Plot histogram of similarities (weights), for 'Neighbors' method.
#'
-#' @param pred Object as returned by \code{computeForecast}
+#' @inheritParams computeError
#' @param index Index in forecasts (integer or date)
#'
#' @export
weights = pred$getParams(index)$weights
if (is.null(weights))
stop("plotSimils only works on 'Neighbors' forecasts")
- par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- hist(pred$getParams(index)$weights, nclass=20, xlab="Poids", ylab="Effectif")
+ par(mfrow=c(1,2), mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
+ small_weights = weights[ weights < 1/length(weights) ]
+ large_weights = weights[ weights >= 1/length(weights) ]
+ hist(small_weights, nclass=25, main="", xlab="Weight < 1/N", ylab="Count")
+ hist(large_weights, nclass=25, main="", xlab="Weight >= 1/N", ylab="Count")
}
#' Functional boxplot
#'
-#' Draw the functional boxplot on the left, and bivariate plot on the right
+#' Draw the functional boxplot on the left, and bivariate plot on the right.
#'
-#' @param data Object return by \code{getData}
-#' @param indices integer or date indices to process
-#' @param plot_bivariate Should the bivariate plot appear?
+#' @inheritParams computeError
+#' @inheritParams plotCurves
#'
#' @export
plotFbox <- function(data, indices=seq_len(data$getSize()))
series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
+ rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
plotlegend=FALSE, lwd=2)
rainbow::fboxplot(series_fds, "bivariate", "hdr", plotlegend=FALSE)
}
#' Compute filaments
#'
-#' Get similar days in the past, as black as distances are small
+#' Obtain similar days in the past, and (optionally) plot them -- as black as distances
+#' are small.
#'
-#' @param data Object as returned by \code{getData}
-#' @param index Index in data (integer or date)
+#' @inheritParams computeError
+#' @param index Index in forecast (integer or date)
#' @param limit Number of neighbors to consider
#' @param plot Should the result be plotted?
#'
#' }
#'
#' @export
-computeFilaments <- function(data, index, limit=60, plot=TRUE)
+computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
{
- ref_serie = data$getCenteredSerie(index)
- if (any(is.na(ref_serie)))
+ weights <- pred$getParams(index)$weights
+ if (is.null(weights) || is.na(pred$getParams(index)$weights[1]))
stop("computeFilaments requires a serie without NAs")
- # Determine indices of no-NAs days followed by no-NAs tomorrows
- fdays = getNoNA2(data, 1, dateIndexToInteger(index,data)-1)
- # Series + tomorrows in columns, ref_serie first
- centered_series = data$getCenteredSeries(fdays)
-
- # Obtain neighbors (closest for euclidian norm)
- L = length(ref_serie)
- distances = sqrt( colSums( (centered_series - ref_serie)^2 / L ) )
- sorted_distances = sort(distances, index.return=TRUE)
-
- # Compute colors for each neighbor (from darkest to lightest)
- nn = min(limit, length(distances))
- min_dist = min(sorted_distances$x[1:nn])
- max_dist = max(sorted_distances$x[1:nn])
- color_values = floor( 19.5 * (sorted_distances$x[1:nn]-min_dist) / (max_dist-min_dist) ) + 1
- colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
+ nn <- min(limit, length(weights))
+ sorted_dists = sort(-log(weights), index.return=TRUE)
+ # Compute colors for each neighbor (from darkest to lightest), if weights differ
+ if ( any( weights != weights[1] ) )
+ {
+ min_dist = min(sorted_dists$x[1:nn])
+ max_dist = max(sorted_dists$x[1:nn])
+ color_values = floor(19.5*(sorted_dists$x[1:nn]-min_dist)/(max_dist-min_dist)) + 1
+ colors = gray.colors(20,0.1,0.9)[color_values] #TODO: 20 == magic number
+ }
+ else
+ colors <- rep(colors()[17], length(weights))
if (plot)
{
# Complete series with (past and present) tomorrows
- ref_serie = c(ref_serie,data$getCenteredSerie(index+1))
- centered_series = rbind( centered_series, data$getCenteredSeries(fdays+1) )
- yrange = quantile(cbind(ref_serie,centered_series), probs=c(0.05,0.95), na.rm=TRUE)
+ ref_serie = c( data$getCenteredSerie( pred$getIndexInData(index)-1 ),
+ data$getCenteredSerie( pred$getIndexInData(index) ) )
+ centered_series = rbind(
+ data$getCenteredSeries( pred$getParams(index)$indices-1 ),
+ data$getCenteredSeries( pred$getParams(index)$indices ) )
+ yrange = range( ref_serie,
+ quantile(centered_series, probs=c(0.025,0.975), na.rm=TRUE) )
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5, lwd=2)
for (i in nn:1)
{
- plot(centered_series[,sorted_distances$ix[i]], ylim=yrange, type="l", col=colors[i],
- xlab=ifelse(i==nn,"Temps (en heures)",""), ylab=ifelse(i==nn,"PM10 centré",""))
+ plot(centered_series[,sorted_dists$ix[i]], ylim=yrange, type="l", col=colors[i],
+ xlab=ifelse(i==1,"Time (hours)",""), ylab=ifelse(i==1,"Centered PM10",""))
par(new=TRUE)
}
# Also plot ref curve, in red
plot(ref_serie, ylim=yrange, type="l", col="#FF0000", xlab="", ylab="")
- abline(v=24, lty=2, col=colors()[56])
+ dot_mark <- 0.5 + which.max( pred$getForecast(1) ==
+ data$getSerie( pred$getIndexInData(1) )[1:length(pred$getForecast(1))] )
+ abline(v=24+dot_mark, lty=2, col=colors()[56], lwd=1)
}
- list("index"=index,"neighb_indices"=fdays[sorted_distances$ix[1:nn]],"colors"=colors)
+ list(
+ "index"=pred$getIndexInData(index),
+ "neighb_indices"=pred$getParams(index)$indices[sorted_dists$ix[1:nn]],
+ "colors"=colors)
}
#' Functional boxplot on filaments
#'
-#' Draw the functional boxplot on filaments obtained by \code{computeFilaments}
+#' Draw the functional boxplot on filaments obtained by \code{computeFilaments()}.
#'
-#' @param data Object return by \code{getData}
+#' @inheritParams computeError
#' @param fil Output of \code{computeFilaments}
+#' @param predict_from First predicted time step
#'
#' @export
-plotFilamentsBox = function(data, fil, ...)
+plotFilamentsBox = function(data, fil, predict_from)
{
if (!requireNamespace("rainbow", quietly=TRUE))
stop("Functional boxplot requires the rainbow package")
series_matrix = rbind(
- data$getSeries(fil$neighb_indices), data$getSeries(fil$neighb_indices+1) )
+ data$getSeries(fil$neighb_indices-1), data$getSeries(fil$neighb_indices) )
series_fds = rainbow::fds(seq_len(nrow(series_matrix)), series_matrix)
+
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
- rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Temps (heures)", ylab="PM10",
+ rainbow::fboxplot(series_fds, "functional", "hdr", xlab="Time (hours)", ylab="PM10",
plotlegend=FALSE, lwd=2)
- # "Magic" found at http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
+ # "Magic": http://stackoverflow.com/questions/13842560/get-xlim-from-a-plot-in-r
usr <- par("usr")
yr <- (usr[4] - usr[3]) / 27
par(new=TRUE)
- plot(data$getSerie(fil$index), type="l", lwd=2, lty=2,
+ plot(c(data$getSerie(fil$index-1),data$getSerie(fil$index)), type="l", lwd=2, lty=2,
ylim=c(usr[3] + yr, usr[4] - yr), xlab="", ylab="")
+ abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}
#' Plot relative conditional variability / absolute variability
#'
#' Draw the relative conditional variability / absolute variability based on filaments
-#' obtained by \code{computeFilaments}
+#' obtained by \code{computeFilaments()}.
#'
-#' @param data Object return by \code{getData}
-#' @param fil Output of \code{computeFilaments}
+#' @inheritParams computeError
+#' @inheritParams plotFilamentsBox
#'
#' @export
-plotRelVar = function(data, fil, ...)
+plotRelVar = function(data, fil, predict_from)
{
- ref_var = c( apply(data$getSeries(fil$neighb_indices),1,sd),
- apply(data$getSeries(fil$neighb_indices+1),1,sd) )
- fdays = getNoNA2(data, 1, fil$index-1)
- global_var = c( apply(data$getSeries(fdays),1,sd), apply(data$getSeries(fdays+1),1,sd) )
+ ref_var = c( apply(data$getSeries(fil$neighb_indices-1),1,sd),
+ apply(data$getSeries(fil$neighb_indices),1,sd) )
+ tdays = .getNoNA2(data, 2, fil$index)
+ global_var = c(
+ apply(data$getSeries(tdays-1),1,sd),
+ apply(data$getSeries(tdays),1,sd) )
yrange = range(ref_var, global_var)
par(mar=c(4.7,5,1,1), cex.axis=1.5, cex.lab=1.5)
plot(ref_var, type="l", col=1, lwd=3, ylim=yrange,
- xlab="Temps (heures)", ylab="Écart-type")
+ xlab="Time (hours)", ylab="Standard deviation")
par(new=TRUE)
plot(global_var, type="l", col=2, lwd=3, ylim=yrange, xlab="", ylab="")
- abline(v=24, lty=2, col=colors()[56])
+ abline(v=24+predict_from-0.5, lty=2, col=colors()[56])
}
projects / talweg.git / blobdiff