# NOT RUN {
set.seed(2)
library(data.table)
signal <- c(
rnorm(25, mean = 10),
rnorm(25, mean = 7),
rnorm(25, mean = 8),
rnorm(25, mean = 5))
#outliers
signal[86] <- 10
labels.dt <- data.table(
start = c(20, 45, 80),
end = c(30, 55, 90),
changes = c(1, 1, 0))
signal.dt <- data.table(
signal,
position=seq_along(signal))
label.colors <- c(
"1"="#ff7d7d",
"0"="#f6c48f")
sig.color <- "grey50"
if(require(ggplot2)){
gg.data <- ggplot()+
geom_rect(aes(
xmin=start, xmax=end,
fill=paste(changes),
ymin=-Inf, ymax=Inf),
alpha=0.5,
data=labels.dt)+
geom_point(aes(
position, signal),
color=sig.color,
data=signal.dt)+
scale_x_continuous(
"position",
breaks=seq(0, 100, by=10))+
scale_fill_manual("label", values=label.colors)+
theme_bw()+
theme(panel.spacing=grid::unit(0, "lines"))
print(gg.data)
}
label.list <- list(
OPART=labels.dt[0],
LOPART=labels.dt)
seg.dt.list <- list()
change.dt.list <- list()
cost.dt.list <- list()
for(model.name in names(label.list)){
label.dt <- data.table(label.list[[model.name]])
fit <- LOPART::LOPART(signal, label.dt, 10)
Algorithm <- factor(model.name, names(label.list))
tau.dt <- fit$cost[, .(
cost_candidates,
tau=0:(.N-1),
change=seq_along(cost_candidates)-0.5
)]
cost.dt.list[[model.name]] <- data.table(Algorithm, tau.dt)
seg.dt.list[[model.name]] <- data.table(Algorithm, fit$segments)
change.dt.list[[model.name]] <- data.table(Algorithm, fit$changes)
}
seg.dt <- do.call(rbind, seg.dt.list)
change.dt <- do.call(rbind, change.dt.list)
cost.dt <- do.call(rbind, cost.dt.list)
algo.sizes <- c(
OPART=1,
LOPART=0.5)
algo.colors <- c(
OPART="deepskyblue",
LOPART="black")
algo.shapes <- c(
OPART=1,
LOPART=2)
if(require(ggplot2)){
gg.data+
scale_size_manual(values=algo.sizes)+
scale_color_manual(values=algo.colors)+
geom_vline(aes(
xintercept=change,
size=Algorithm,
color=Algorithm),
data=change.dt)+
geom_segment(aes(
start-0.5, mean,
size=Algorithm,
color=Algorithm,
xend=end+0.5, yend=mean),
data=seg.dt)
}
if(require(ggplot2)){
ggplot()+
geom_rect(aes(
xmin=start, xmax=end,
fill=paste(changes),
ymin=-Inf, ymax=Inf),
alpha=0.5,
data=labels.dt)+
scale_fill_manual("label", values=label.colors)+
theme_bw()+
theme(panel.spacing=grid::unit(0, "lines"))+
scale_x_continuous(
"position",
breaks=seq(0, 100, by=10))+
geom_point(aes(
change, cost_candidates,
color=Algorithm, shape=Algorithm),
data=cost.dt)+
scale_color_manual(values=algo.colors)+
scale_shape_manual(values=algo.shapes)
}
abbrev.vec <- c(
data="data and models",
cost="cost of last change")
yfac <- function(l){
factor(abbrev.vec[[l]], abbrev.vec)
}
COST <- function(dt){
data.table(y.var=yfac("cost"), dt)
}
DATA <- function(dt){
data.table(y.var=yfac("data"), dt)
}
if(require(ggplot2)){
ggplot()+
geom_rect(aes(
xmin=start, xmax=end,
fill=paste(changes),
ymin=-Inf, ymax=Inf),
alpha=0.5,
data=labels.dt)+
scale_fill_manual("label", values=label.colors)+
theme_bw()+
theme(panel.spacing=grid::unit(0, "lines"))+
facet_grid(y.var ~ ., scales="free")+
geom_vline(aes(
xintercept=change,
size=Algorithm,
color=Algorithm),
data=change.dt)+
geom_segment(aes(
start-0.5, mean,
size=Algorithm,
color=Algorithm,
xend=end+0.5, yend=mean),
data=DATA(seg.dt))+
geom_point(aes(
position, signal),
color=sig.color,
shape=1,
data=DATA(signal.dt))+
scale_size_manual(values=algo.sizes)+
scale_color_manual(values=algo.colors)+
scale_shape_manual(values=algo.shapes)+
ylab("")+
scale_x_continuous(
"position",
breaks=seq(0, 100, by=10))+
geom_point(aes(
change, cost_candidates,
color=Algorithm, shape=Algorithm),
data=COST(cost.dt))
}
# }
Run the code above in your browser using DataLab