LOPART: Labeled Optimal PARTitioning

Description Usage Arguments Details Value Author(s) Examples

Description

Compute an optimal segmentation (change in Gaussian mean model, square loss), which is consistent with the given labels, and with a penalty for each changepoint outside of labeled regions.

Usage

1
2
3
4
5
6
7
LOPART(
  x,
  labels,
  penalty_unlabeled,
  n_updates = length(x),
  penalty_labeled = penalty_unlabeled
)

Arguments

x

numeric vector of data to fit a Gaussian mean model.

labels

data frame with at least three columns: start, end, changes. start/end should be indices of x, from 1 to length(x). changes should be either 0 or 1. The prediced changepoints are guaranteed to be consistent with these labels.

penalty_unlabeled

non-negative penalty constant (larger for fewer changes, smaller for more changes). penalty=0 means a change in every unlabeled region, penalty=Inf means no changes in unlabeled regions.

n_updates

how many dynamic programming updates to compute? Must be at least 1 and at most length(x).

penalty_labeled

non-negative penalty constant to use for changes in positive labels.

Details

Provides a high-level interface to LOPART_interface R function and LOPART C code.

Value

list with named elements, all of which are data tables. loss has one row with loss/cost values. cost is the output from LOPART_interface. changes has one row for each predicted changepoint (e.g. change=1.5 means a change between data points 1 and 2). segments has one row for each segment.

Author(s)

Toby Dylan Hocking

Examples

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
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))
}

LOPART documentation built on July 1, 2020, 5:23 p.m.

Related to LOPART in LOPART...