X-Git-Url: https://git.auder.net/?p=talweg.git;a=blobdiff_plain;f=pkg%2FR%2Fplot.R;h=0f895bd16483795302623b53ea983f3ab9c2cd92;hp=372abf0a99e086cceb9fec1dc5f9da9c5907da90;hb=HEAD;hpb=6d50a76fc27236cb056a479be395ebe467666c8b

diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index 372abf0..0f895bd 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -1,8 +1,8 @@
 #' 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
@@ -11,93 +11,91 @@ plotCurves <- function(data, indices=seq_len(data$getSize()))
 	series = data$getSeries(indices)
 	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
@@ -106,17 +104,19 @@ plotSimils <- function(pred, index)
 	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$getSize()))
@@ -132,17 +132,18 @@ plotFbox <- function(data, indices=seq_len(data$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?
 #'
@@ -154,100 +155,109 @@ plotFbox <- function(data, indices=seq_len(data$getSize()))
 #' }
 #'
 #' @export
-computeFilaments <- function(data, index, limit=60, plot=TRUE)
+computeFilaments <- function(data, pred, index, limit=60, plot=TRUE)
 {
-	ref_serie = data$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(0.025,0.975), 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], lwd=1)
+		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(c(data$getSerie(fil$index),data$getSerie(fil$index+1)), 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, lty=2, col=colors()[56])
+	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])
 }