<!--
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Comparing sizes when training on same or other groups}
-->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  fig.width=6,
  fig.height=6)
data.table::setDTthreads(1)
## output: rmarkdown::html_vignette above creates html where figures are limited to 700px wide.
## Above CSS from https://stackoverflow.com/questions/34906002/increase-width-of-entire-html-rmarkdown-output main-container is for html_document, body is for html_vignette
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

The goal of this vignette is explain how to use
`ResamplingSameOtherSizesCV` for various kinds of cross-validation.

# Simulations

We begin with a simple simulated data set.

## Comparing training on Same/Other/All subsets

```{r simulationScatter}
N <- 2100
abs.x <- 70
set.seed(2)
x.vec <- runif(N, -abs.x, abs.x)
str(x.vec)
library(data.table)
(task.dt <- data.table(
  x=x.vec,
  y = sin(x.vec)+rnorm(N,sd=0.5)))
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      x, y),
      shape=1,
      data=task.dt)+
    coord_equal()
}
```

Above we see a scatterplot of the simulated data. The goal of the
learning algorithm will be to predict y from x.

The code below assigns three test groups to the randomly simulated
data.

```{r}
atomic.group.size <- 2
task.dt[, agroup := rep(seq(1, N/atomic.group.size), each=atomic.group.size)][]
task.dt[, random_group := rep(
  rep(c("A","B","B","C","C","C","C"), each=atomic.group.size),
  l=.N
)][]
table(group.tab <- task.dt$random_group)
```

The output above shows the number of rows in each random group. 
Below we define a task,

```{r}
reg.task <- mlr3::TaskRegr$new(
  "sin", task.dt, target="y")
reg.task$col_roles$group <- "agroup"
reg.task$col_roles$stratum <- "random_group"
reg.task$col_roles$feature <- "x"
```

Note that if we assign the `subset` role at this point, we will get an
error, because this is not a standard mlr3 role.


```{r error=TRUE, purl=FALSE}
reg.task$col_roles$subset <- "random_group" 
```

Below we define the cross-validation object, which loads the
mlr3resampling package, and then we assign the random group column to
be used as the `subset` role.

```{r}
same_other_sizes_cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
reg.task$col_roles$subset <- "random_group" 
```

Below we instantiate the resampler, in
order to show details about how it works (but normally you should not
instantiate it yourself, as this will be done automatically inside the
call to `mlr3::benchmark`).

```{r}
same_other_sizes_cv$instantiate(reg.task)
same_other_sizes_cv$instance$iteration.dt
```

So using the K-fold cross-validation, we will do one train/test split
for each row of the table above. There is one row for each combination
of test subset (A, B, C), train subset (same, other, all), and test fold
(1, 2, 3).

We compute and plot the results using the code below,

```{r SameOtherCV}
(reg.learner.list <- list(
  mlr3::LearnerRegrFeatureless$new()))
if(requireNamespace("rpart")){
  reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
}
(same.other.grid <- mlr3::benchmark_grid(
  reg.task,
  reg.learner.list,
  same_other_sizes_cv))
##if(require(future))plan("multisession")
lgr::get_logger("mlr3")$set_threshold("warn")
(same.other.result <- mlr3::benchmark(
  same.other.grid, store_models = TRUE))
same.other.score <- mlr3resampling::score(
  same.other.result, mlr3::msr("regr.rmse"))
plot(same.other.score)
```

The plot method above shows a multi-panel figure (vertical facet for
each algorithm), whereas below we make a custom ggplot with no
vertical facets, and color for algorithm.

```{r}
same.other.score[, n.train := sapply(train, length)]
same.other.score[1]
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      regr.rmse, train.subsets, color=algorithm),
      shape=1,
      data=same.other.score)+
    geom_text(aes(
      Inf, train.subsets,
      label=sprintf("n.train=%d ", n.train)),
      hjust=1,
      vjust=1.5,
      data=same.other.score[algorithm=="featureless" & test.fold==1])+
    facet_grid(. ~ test.subset, labeller=label_both, scales="free")+
    scale_x_continuous(
      "Root mean squared prediction error (test set)")
}
```

The figure above shows the effect of train set size on test error.

```{r}
same.other.wide <- dcast(
  same.other.score,
  algorithm + test.subset + train.subsets ~ .,
  list(mean, sd),
  value.var="regr.rmse")
if(require(ggplot2)){
  ggplot()+
    geom_segment(aes(
      regr.rmse_mean+regr.rmse_sd, train.subsets,
      xend=regr.rmse_mean-regr.rmse_sd, yend=train.subsets,
      color=algorithm),
      data=same.other.wide)+
    geom_point(aes(
      regr.rmse_mean, train.subsets, color=algorithm),
      shape=1,
      data=same.other.wide)+
    geom_text(aes(
      Inf, train.subsets,
      label=sprintf("n.train=%d ", n.train)),
      hjust=1,
      vjust=1.5,
      data=same.other.score[algorithm=="featureless" & test.fold==1])+
    facet_grid(. ~ test.subset, labeller=label_both, scales="free")+
    scale_x_continuous(
      "Root mean squared prediction error (test set)")
}
```

The figure above shows a test subset in each panel, the train subsets on
the y axis, the test error on the x axis, the two different algorithms
are shown in two different colors. We can clearly see that 

* For `train.subsets=same`, test error is largest, sometimes almost as
  large as featureless, which is the error rate when no relationship
  has been learned between inputs and outputs (not enough data).
* For `train.subsets=other`, rpart test error is significantly smaller
  than featureless, indicating that some non-trivial relationship
  between inputs and outputs has been learned. Sometimes other has
  larger error than same, sometimes smaller (depending on sample
  size).
* For `train.subsets=all`, rpart test error tends to be minimal, which
  indicates that combining all of the subsets is beneficial in this
  case (when the pattern is exactly the same in the different
  subsets).
  
Overall in the plot above, all tends to have less prediction error
than same, which suggests that the subsets are similar (and indeed the
subsets are i.i.d. in this simulation). Another visualization method
is shown below,

```{r}
plist <- mlr3resampling::pvalue(same.other.score, digits=3)
plot(plist)
```

The visualization above includes P-values (two-sided T-test) for the
differences between Same and Other/All.
  
Below we visualize test error as a function of train size.

```{r}
if(require(ggplot2)){
  ggplot()+
    geom_line(aes(
      n.train, regr.rmse,
      color=algorithm,
      group=paste(algorithm, test.fold)),
      data=same.other.score)+
    geom_label(aes(
      n.train, regr.rmse,
      color=algorithm,
      label=train.subsets),
      data=same.other.score)+
    facet_grid(. ~ test.subset, labeller=label_both, scales="free")+
    scale_y_continuous(
      "Root mean squared prediction error (test set)")
}
```

## Downsample to see how many train data are required for good accuracy overall

In the previous section we defined a task using the `subset` role,
which means that the different values in that column will be used to
define different subsets for training/testing using same/other/all CV.
In contrast, below we define a task without the `subset` role, which
means that we will not have separate CV iterations for same/other/all
(full data is treated as one subset / train subset is same).

```{r}
task.no.subset <- mlr3::TaskRegr$new(
  "sin", task.dt, target="y")
task.no.subset$col_roles$group <- "agroup"
task.no.subset$col_roles$stratum <- "random_group"
task.no.subset$col_roles$feature <- "x"
str(task.no.subset$col_roles)
```

Below we define cross-validation, and we set the `sizes` to 5 so we
can see what happens when we have have train sets that are 5 sizes
smaller than the full train set size.

```{r}
same_other_sizes_cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
same_other_sizes_cv$param_set$values$sizes <- 5
same_other_sizes_cv$instantiate(task.no.subset)
same_other_sizes_cv$instance$iteration.dt
```

So using the K-fold cross-validation, we will do one train/test split
for each row of the table above. There is one row for each combination
of `n.train.groups` (full train set size + 5 smaller sizes), and test
fold (1, 2, 3).

We compute and plot the results using the code below,

```{r}
(reg.learner.list <- list(
  mlr3::LearnerRegrFeatureless$new()))
if(requireNamespace("rpart")){
  reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
}
(same.other.grid <- mlr3::benchmark_grid(
  task.no.subset,
  reg.learner.list,
  same_other_sizes_cv))
##if(require(future))plan("multisession")
lgr::get_logger("mlr3")$set_threshold("warn")
(same.other.result <- mlr3::benchmark(
  same.other.grid, store_models = TRUE))
same.other.score <- mlr3resampling::score(
  same.other.result, mlr3::msr("regr.rmse"))
same.other.score[, n.train := sapply(train, length)]
same.other.score[1]

if(require(ggplot2)){
  ggplot()+
    geom_line(aes(
      n.train, regr.rmse,
      color=algorithm,
      group=paste(algorithm, test.fold)),
      data=same.other.score)+
    geom_point(aes(
      n.train, regr.rmse,
      color=algorithm),
      data=same.other.score)+
    facet_grid(. ~ test.subset, labeller=label_both, scales="free")+
    scale_x_log10(
      "Number of train rows",
      breaks=unique(same.other.score$n.train))+
    scale_y_continuous(
      "Root mean squared prediction error (test set)")
}
```

From the plot above, it looks like about 700 rows is enough to get
minimal test error, using the rpart learner.

  
## Downsample to sizes of other sets

```{r simulationShort}
N <- 600
abs.x <- 20
set.seed(1)
x.vec <- sort(runif(N, -abs.x, abs.x))
str(x.vec)
library(data.table)
(task.dt <- data.table(
  x=x.vec,
  y = sin(x.vec)+rnorm(N,sd=0.5)))
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      x, y),
      shape=1,
      data=task.dt)+
    coord_equal()
}
atomic.subset.size <- 2
task.dt[, agroup := rep(seq(1, N/atomic.subset.size), each=atomic.subset.size)][]
task.dt[, random_subset := rep(
  rep(c("A","B","B","B"), each=atomic.subset.size),
  l=.N
)][]
table(subset.tab <- task.dt$random_subset)

reg.task <- mlr3::TaskRegr$new(
  "sin", task.dt, target="y")
reg.task$col_roles$subset <- "random_subset"
reg.task$col_roles$group <- "agroup"
reg.task$col_roles$stratum <- "random_subset"
reg.task$col_roles$feature <- "x"
same_other_sizes_cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
```

In the previous section we analyzed prediction accuracy of
same/other/all, which corresponds to keeping `sizes` parameter at
default of -1.  The main difference in this section is that we change
`sizes` to 0, which means to down-sample same/other/all, so we can see
if there is an effect for sample size (there should be for iid
problems with intermediate difficulty). We set sizes to 0 in the next
line:

```{r}
same_other_sizes_cv$param_set$values$sizes <- 0
same_other_sizes_cv$instantiate(reg.task)
same_other_sizes_cv$instance$it
(reg.learner.list <- list(
  mlr3::LearnerRegrFeatureless$new()))
if(requireNamespace("rpart")){
  reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
}
(same.other.grid <- mlr3::benchmark_grid(
  reg.task,
  reg.learner.list,
  same_other_sizes_cv))
##if(require(future))plan("multisession")
lgr::get_logger("mlr3")$set_threshold("warn")
(same.other.result <- mlr3::benchmark(
  same.other.grid, store_models = TRUE))
same.other.score <- mlr3resampling::score(
  same.other.result, mlr3::msr("regr.rmse"))
same.other.score[1]
```

The plot below shows the same results (no down-sampling) as if we did
`sizes=-1` (like in the previous section.

```{r}
if(require(ggplot2)){
ggplot()+
  geom_point(aes(
    regr.rmse, train.subsets, color=algorithm),
    shape=1,
    data=same.other.score[groups==n.train.groups])+
  facet_grid(. ~ test.subset, labeller=label_both)
}
```

The plots below compare all six train subsets (including three
down-sampled), and it it is clear there is an effect for sample size.

```{r}
same.other.score[, subset.N := paste(train.subsets, n.train.groups)][]
(levs <- same.other.score[order(train.subsets, n.train.groups), unique(subset.N)])
same.other.score[, subset.N.fac := factor(subset.N, levs)]
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      regr.rmse, subset.N.fac, color=algorithm),
      shape=1,
      data=same.other.score)+
    facet_wrap("test.subset", labeller=label_both, scales="free", nrow=1)
}
(levs <- same.other.score[order(n.train.groups, train.subsets), unique(subset.N)])
same.other.score[, N.subset.fac := factor(subset.N, levs)]
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      regr.rmse, N.subset.fac, color=algorithm),
      shape=1,
      data=same.other.score)+
    facet_wrap("test.subset", labeller=label_both, scales="free", nrow=1)
}
```

Another way to view the effect of sample size is to plot the
test/prediction error, as a function of number of train data, as in
the plots below.

```{r}
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      n.train.groups, regr.rmse,
      color=train.subsets),
      shape=1,
      data=same.other.score)+
    geom_line(aes(
      n.train.groups, regr.rmse,
      group=paste(train.subsets, seed, algorithm),
      linetype=algorithm,
      color=train.subsets),
      data=same.other.score)+
    facet_grid(test.fold ~ test.subset, labeller=label_both)
}
rpart.score <- same.other.score[algorithm=="rpart" & train.subsets != "other"]
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      n.train.groups, regr.rmse,
      color=train.subsets),
      shape=1,
      data=rpart.score)+
    geom_line(aes(
      n.train.groups, regr.rmse,
      group=paste(train.subsets, seed, algorithm),
      color=train.subsets),
      data=rpart.score)+
    facet_grid(test.fold ~ test.subset, labeller=label_both)
}
```

## Use with auto_tuner on a task with stratification and grouping

In this section we show how `ResamplingSameOtherSizesCV` can be used on a task with stratification and grouping, for hyper-parameter learning. First we recall the previously defined task and evaluation CV.

```{r}
str(reg.task$col_roles)
```

We see in the output aove that the task has column roles for both
`stratum` and `group`, which normally errors when used with
`ResamplingCV`:

```{r error=TRUE, purl=FALSE}
mlr3::ResamplingCV$new()$instantiate(reg.task)
```

Below we show how `ResamplingSameOtherSizesCV` can be used instead:

```{r}
ignore.cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
ignore.cv$param_set$values$ignore_subset <- TRUE
ignore.cv$instantiate(reg.task)
ignore.cv$instance$iteration.dt
```

To use the above CV object with a learning algorithm in a benchmark
experiment, we need to use it as the `resampling` argument to
`auto_tuner`, as in the code below,

```{r}
do_benchmark <- function(subtrain.valid.cv){
  reg.learner.list <- list(
    mlr3::LearnerRegrFeatureless$new())
  if(requireNamespace("rpart")){
    reg.learner.list$rpart <- mlr3::LearnerRegrRpart$new()
    if(requireNamespace("mlr3tuning")){
      rpart.learner <- mlr3::LearnerRegrRpart$new()
      ##mlr3tuningspaces::lts(rpart.learner)$param_set$values
      rpart.learner$param_set$values$cp <- paradox::to_tune(1e-4, 0.1, log=TRUE)
      reg.learner.list$rpart.tuned <- mlr3tuning::auto_tuner(
        tuner = mlr3tuning::tnr("grid_search"), #mlr3tuning::TunerBatchGridSearch$new()
        learner = rpart.learner,
        resampling = subtrain.valid.cv,
        measure = mlr3::msr("regr.rmse"))
    }
  }
  same.other.grid <- mlr3::benchmark_grid(
    reg.task,
    reg.learner.list,
    same_other_sizes_cv)
  lgr::get_logger("bbotk")$set_threshold("warn")
  same.other.result <- mlr3::benchmark(
    same.other.grid, store_models = TRUE)
}
```

```{r error=TRUE, purl=FALSE}
do_benchmark(mlr3::ResamplingCV$new())
```

The error above is because `ResamplingCV` does not support
stratification and grouping. To fix that, we can use the code below:

```{r}
ignore.cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
ignore.cv$param_set$values$ignore_subset <- TRUE
(same.other.result <- do_benchmark(ignore.cv))
```

The output above shows that the benchmark worked.
The code below plots the results.

```{r}
same.other.score <- mlr3resampling::score(
  same.other.result, mlr3::msr("regr.rmse"))
same.other.score[1]
same.other.wide <- dcast(
  same.other.score,
  algorithm + test.subset + train.subsets ~ .,
  list(mean, sd),
  value.var="regr.rmse")
if(require(ggplot2)){
  ggplot()+
    geom_segment(aes(
      regr.rmse_mean+regr.rmse_sd, train.subsets,
      xend=regr.rmse_mean-regr.rmse_sd, yend=train.subsets),
      data=same.other.wide)+
    geom_point(aes(
      regr.rmse_mean, train.subsets),
      shape=1,
      data=same.other.wide)+
    facet_grid(algorithm ~ test.subset, labeller=label_both)
}
```

The plot above has different panels for `rpart` (without tuning) and
`tuned` (rpart with tuning of `cp`).

## Conclusions

`mlr3resampling::ResamplingSameOtherSizesCV` can be used for model evaluation (train/test split):

* compare prediction accuracy of models trained on same/other/all subsets (need to set column role `subset`).
* compare prediction accuracy of models trained on down-sampled subsets (need to set param `sizes`).

It can also be used for model training (subtrain/validation split):

* to learn regularization hyper-parameters, on a task with both `stratum` and `group` roles (use is as `resampling` argument of `auto_tuner`).

# Arizona trees data

The goal of this section is explain the differences between various
column roles:

* `group` is used to designate observations which should stay together
  when splitting. In other words, two rows in the same `group` should
  never appear in different sets.
* `subset` designates a column whose values are each treated as a test
  set (the train data come from Same/Other/All subsets).

## What is a group?

Below we load the data set.

```{r}
data(AZtrees,package="mlr3resampling")
library(data.table)
AZdt <- data.table(AZtrees)
AZdt[1]
```

Above we see one row of data. 
Below we see a scatterplot of the data:

* Every row is a labeled pixel.
* Every dot is plotted at the xcoord/ycoord (lat/long) position on a
  map around Flagstaff, AZ.

```{r}
x.center <- -111.72
y.center <- 35.272
rect.size <- 0.01/2
x.min.max <- x.center+c(-1, 1)*rect.size
y.min.max <- y.center+c(-1, 1)*rect.size
rect.dt <- data.table(
  xmin=x.min.max[1], xmax=x.min.max[2],
  ymin=y.min.max[1], ymax=y.min.max[2])
if(require(ggplot2)){
  tree.fill.scale <- scale_fill_manual(
    values=c(Tree="black", "Not tree"="white"))
  ggplot()+
    theme_bw()+
    tree.fill.scale+
    geom_rect(aes(
      xmin=xmin, xmax=xmax, ymin=ymin,ymax=ymax),
      data=rect.dt,
      fill="red",
      linewidth=3,
      color="red")+
    geom_point(aes(
      xcoord, ycoord, fill=y),
      shape=21,
      data=AZdt)+
    coord_equal()
}
```

Note the red square in the plot above. Below we zoom into that square.

```{r}
if(require(ggplot2)){
  gg <- ggplot()+
    theme_bw()+
    tree.fill.scale+
    geom_point(aes(
      xcoord, ycoord, fill=y),
      shape=21,
      data=AZdt)+
    coord_equal()+
    scale_x_continuous(
      limits=x.min.max)+
    scale_y_continuous(
      limits=y.min.max)
  if(require(directlabels)){
    gg <- gg+geom_dl(aes(
      xcoord, ycoord, label=polygon),
      data=AZdt,
      method="smart.grid")
  }
  gg
}
```

In the plot above, we see that there are several groups of points,
each with a black number. Each group of points comes from a single
polygon (label drawn in GIS software), and the black number is the
polygon ID number. So each polygon represents one label, either tree
or not, and there are one or more points/pixels with that label inside
each polygon.

A polygon is an example of a group. Each polygon results in one or
more rows of training data (pixels), but since pixels in a given group
were all labeled together, we would like to keep them together when
splitting the data.

## What is a subset?

Below we plot the same data, but this time colored by region.

```{r}
##dput(RColorBrewer::brewer.pal(3,"Dark2"))
region.colors <- c(NW="#1B9E77", NE="#D95F02", S="#7570B3")
if(require(ggplot2)){
  ggplot()+
    theme_bw()+
    tree.fill.scale+
    scale_color_manual(
      values=region.colors)+
    geom_point(aes(
      xcoord, ycoord, color=region3, fill=y),
      shape=21,
      data=AZdt)+
    coord_equal()
}
```

We can see in the plot above that there are three values in the
`region3` column: NE, NW, and S (different geographical regions on the
map which are well-separated). We would like to know if it is possible
to train on one region, and then accurately predict on another region.

## Cross-validation

First we create a task:

```{r}
ctask <- mlr3::TaskClassif$new(
  "AZtrees", AZdt, target="y")
ctask$col_roles$subset <- "region3"
ctask$col_roles$group <- "polygon"
ctask$col_roles$stratum <- "y"
ctask$col_roles$feature <- grep("SAMPLE",names(AZdt),value=TRUE)
str(ctask$col_roles)
```

Then we can instantiate the CV to see how it works (but usually you do
not need to instantiate, if you are using `benchmark` it does it for
you).

```{r}
same.other.cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
same.other.cv$param_set$values$folds <- 3
same.other.cv$instantiate(ctask)
same.other.cv$instance$iteration.dt[, .(
  train.subsets, test.fold, test.subset, n.train.groups,
  train.rows=sapply(train, length))]
```

The table above has one row per train/test split for which
error/accuracy metrics will be computed.  The `n.train.groups` column
is the number of polygons which are used in the train set, which is
defined as the intersection of the train subsets and the train folds.
To double check, below we compute the total number of groups/polygons per
subset/region, and the expected number of train groups/polygons.

```{r}
AZdt[, .(
  polygons=length(unique(polygon))
), by=region3][
, train.polygons := polygons*with(same.other.cv$param_set$values, (folds-1)/folds)
][]
```

It is clear that the counts in the `train.polygons` column above match
the numbers in the previous table column `n.train.groups`. To
determine the number of rows of train data, we can look at the
`train.rows` column in the previous table.

## Benchmark and test error computation

Below we define the benchmark experiment.

```{r}
same.other.cv <- mlr3resampling::ResamplingSameOtherSizesCV$new()
(learner.list <- list(
  mlr3::LearnerClassifFeatureless$new()))
if(requireNamespace("rpart")){
  learner.list$rpart <- mlr3::LearnerClassifRpart$new()
}
for(learner.i in seq_along(learner.list)){
  learner.list[[learner.i]]$predict_type <- "prob"
}
(bench.grid <- mlr3::benchmark_grid(ctask, learner.list, same.other.cv))
```

Above we see one row per combination of task, learner, and resampling.
Below we compute the benchmark result and test accuracy.

```{r}
bench.result <- mlr3::benchmark(bench.grid)
measure.list <- mlr3::msrs(c("classif.acc","classif.auc"))
score.dt <- mlr3resampling::score(bench.result, measure.list)
score.dt[1]
```

Above we see one row of the result, for one train/test split.
Below we plot the accuracy results using two different methods.

```{r}
score.long <- melt(
  score.dt,
  measure.vars=measure(variable, pattern="classif.(acc|auc)"))
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      value, train.subsets, color=algorithm),
      data=score.long)+
    facet_grid(test.subset ~ variable, labeller=label_both, scales="free")
}
```

Above we show one dot per train/test split, and another way to do that is via the plot method, as below.

```{r}
plot(score.dt)
```

Below we take the
mean/SD over folds.

```{r}
score.wide <- dcast(
  score.long,
  algorithm + test.subset + train.subsets + variable ~ .,
  list(mean, sd),
  value.var="value")
if(require(ggplot2)){
  ggplot()+
    geom_point(aes(
      value_mean, train.subsets, color=algorithm),
      size=3,
      fill="white",
      shape=21,
      data=score.wide)+
    geom_segment(aes(
      value_mean+value_sd, train.subsets,
      color=algorithm,
      linewidth=algorithm,
      xend=value_mean-value_sd, yend=train.subsets),
      data=score.wide)+
    scale_linewidth_manual(values=c(featureless=2, rpart=1))+
    facet_grid(test.subset ~ variable, labeller=label_both, scales="free")+
    scale_x_continuous(
      "Mean +/- SD of test accuracy/AUC over folds/splits")
}
```

The plot above shows an interesting pattern:

* For test subsets NE and NW, training on other subsets is less
  accurate than training on the same subset. Training on All subsets
  is no more accurate than training on the same subset. These results
  suggest that learnable patterns in other subsets are too different
  to be beneficial for predicting on these subsets.
* For test subset S, training on other subsets is slightly more
  accurate than training on the same subset, and training on all
  subsets is slightly more accurate still. These results suggest that
  the learnable pattern is similar enough in the other subsets so as
  to be beneficial for prediction in subset S.

Another way to visualize these patterns is via the plot method for pvalue objects, as below.

```{r}
AZ_pval <- mlr3resampling::pvalue(score.dt, digits=3)
plot(AZ_pval)
```

The figure above shows P-values for classification accuracy (by
default the first measure is used). If we want to compute P-values for
AUC, we can use the code below:

```{r}
AZ_pval_AUC <- mlr3resampling::pvalue(score.dt, "classif.auc", digits=3)
plot(AZ_pval_AUC)
```

## Conclusion

Column roles `group`, `stratum`, and `subset` may be used together, in
the same task, in order to perform a cross-validation experiment which
captures the structure in the data.


# Session info

```{r}
sessionInfo()
```

