The goal of this vignette is to explain the older resamplers:
ResamplingVariableSizeTrainCV and ResamplingSameOtherCV, which
output some data which are useful for visualizing the train/test
splits. If you do not want to visualize the train/test splits, then it
is recommended to instead use the newere resampler,
ResamplingSameOtherSizesCV (see other vignette).
Same/Other/All resampler
The goal of thie section is to explain how to quantify the extent to which it is possible to train on one data subset, and predict on another data subset. This kind of problem occurs frequently in many different problem domains:
- geography: can we train on one region (say Europe) and accurately predict on another? (North America)
- time series: can we train on one time period (2000) and accurately predict on another? (2001)
- personalization: can we train on one person (Alice) and accurately predict on another? (Bob)
The ideas are similar to my previous blog posts about how to do this
in
python
and R. Below
we explain how to use mlr3resampling for this purpose, in simulated
regression and classification problems. To use this method in
real data, the important sections to read below are named “Benchmark:
computing test error,” which show how to create these cross-validation
experiments using mlr3 code.
Simulated regression problems
We begin by generating some data which can be used with regression algorithms. Assume there is a data set with some rows from one person, some rows from another,
N <- 300
library(data.table)
set.seed(1)
abs.x <- 2
reg.dt <- data.table(
x=runif(N, -abs.x, abs.x),
person=rep(1:2, each=0.5*N))
reg.pattern.list <- list(
easy=function(x, person)x^2,
impossible=function(x, person)(x^2+person*3)*(-1)^person)
reg.task.list <- list()
for(task_id in names(reg.pattern.list)){
f <- reg.pattern.list[[task_id]]
yname <- paste0("y_",task_id)
reg.dt[, (yname) := f(x,person)+rnorm(N)][]
task.dt <- reg.dt[, c("x","person",yname), with=FALSE]
reg.task <- mlr3::TaskRegr$new(
task_id, task.dt, target=yname)
reg.task$col_roles$subset <- "person"
reg.task$col_roles$stratum <- "person"
reg.task$col_roles$feature <- "x"
reg.task.list[[task_id]] <- reg.task
}
reg.dt
#> x person y_easy y_impossible
#> <num> <int> <num> <num>
#> 1: -0.9379653 1 1.32996609 -2.918082
#> 2: -0.5115044 1 0.24307692 -3.866062
#> 3: 0.2914135 1 -0.23314657 -3.837799
#> 4: 1.6328312 1 1.73677545 -7.221749
#> 5: -1.1932723 1 -0.06356159 -5.877792
#> ---
#> 296: 0.7257701 2 -2.48130642 5.180948
#> 297: -1.6033236 2 1.20453459 9.604312
#> 298: -1.5243898 2 1.89966190 7.511988
#> 299: -1.7982414 2 3.47047566 11.035397
#> 300: 1.7170157 2 0.60541972 10.719685
The table above shows some simulated data for two regression problems:
- easy problem has the same pattern for each person, so it is possible/easy to train on one person, and accurately predict on another.
- impossible problem has a different pattern for each person, so it is impossible to train on one person, and accurately predict on another.
- when adapting the code above to real data, the important part is the
mlr3::TaskRegrline which tells mlr3 what data set to use, what is the target column, and what is the subset/stratum column.
Static visualization of simulated data
First we reshape the data using the code below,
(reg.tall <- nc::capture_melt_single(
reg.dt,
task_id="easy|impossible",
value.name="y"))
#> x person task_id y
#> <num> <int> <char> <num>
#> 1: -0.9379653 1 easy 1.32996609
#> 2: -0.5115044 1 easy 0.24307692
#> 3: 0.2914135 1 easy -0.23314657
#> 4: 1.6328312 1 easy 1.73677545
#> 5: -1.1932723 1 easy -0.06356159
#> ---
#> 596: 0.7257701 2 impossible 5.18094849
#> 597: -1.6033236 2 impossible 9.60431191
#> 598: -1.5243898 2 impossible 7.51198770
#> 599: -1.7982414 2 impossible 11.03539747
#> 600: 1.7170157 2 impossible 10.71968480
The table above is a more convenient form for the visualization which we create using the code below,
if(require(animint2)){
ggplot()+
geom_point(aes(
x, y),
data=reg.tall)+
facet_grid(
task_id ~ person,
labeller=label_both,
space="free",
scales="free")+
scale_y_continuous(
breaks=seq(-100, 100, by=2))
}
#> Le chargement a nécessité le package : animint2
#> Registered S3 methods overwritten by 'animint2':
#> method from
#> [.uneval ggplot2
#> drawDetails.zeroGrob ggplot2
#> grid.draw.absoluteGrob ggplot2
#> grobHeight.absoluteGrob ggplot2
#> grobHeight.zeroGrob ggplot2
#> grobWidth.absoluteGrob ggplot2
#> grobWidth.zeroGrob ggplot2
#> grobX.absoluteGrob ggplot2
#> grobY.absoluteGrob ggplot2
#> heightDetails.titleGrob ggplot2
#> heightDetails.zeroGrob ggplot2
#> makeContext.dotstackGrob ggplot2
#> print.element ggplot2
#> print.ggplot2_bins ggplot2
#> print.rel ggplot2
#> print.theme ggplot2
#> print.uneval ggplot2
#> widthDetails.titleGrob ggplot2
#> widthDetails.zeroGrob ggplot2
#>
#> Attachement du package : 'animint2'
#> Les objets suivants sont masqués depuis 'package:ggplot2':
#>
#> %+%, %+replace%, Coord, CoordCartesian, CoordFixed, CoordFlip,
#> CoordMap, CoordPolar, CoordQuickmap, CoordTrans, Geom, GeomAbline,
#> GeomAnnotationMap, GeomArea, GeomBar, GeomBlank, GeomContour,
#> GeomCrossbar, GeomCurve, GeomCustomAnn, GeomDensity, GeomDensity2d,
#> GeomDotplot, GeomErrorbar, GeomErrorbarh, GeomHex, GeomHline,
#> GeomLabel, GeomLine, GeomLinerange, GeomLogticks, GeomMap,
#> GeomPath, GeomPoint, GeomPointrange, GeomPolygon, GeomRaster,
#> GeomRasterAnn, GeomRect, GeomRibbon, GeomRug, GeomSegment,
#> GeomSmooth, GeomSpoke, GeomStep, GeomText, GeomTile, GeomViolin,
#> GeomVline, Position, PositionDodge, PositionFill, PositionIdentity,
#> PositionJitter, PositionJitterdodge, PositionNudge, PositionStack,
#> Scale, ScaleContinuous, ScaleContinuousDate,
#> ScaleContinuousDatetime, ScaleContinuousIdentity,
#> ScaleContinuousPosition, ScaleDiscrete, ScaleDiscreteIdentity,
#> ScaleDiscretePosition, Stat, StatBin, StatBin2d, StatBindot,
#> StatBinhex, StatContour, StatCount, StatDensity, StatDensity2d,
#> StatEcdf, StatEllipse, StatFunction, StatIdentity, StatQq,
#> StatSmooth, StatSum, StatSummary, StatSummary2d, StatSummaryBin,
#> StatSummaryHex, StatUnique, StatYdensity, aes, aes_, aes_all,
#> aes_auto, aes_q, aes_string, annotate, annotation_custom,
#> annotation_logticks, annotation_map, annotation_raster,
#> as_labeller, autoplot, benchplot, borders, calc_element,
#> continuous_scale, coord_cartesian, coord_equal, coord_fixed,
#> coord_flip, coord_map, coord_munch, coord_polar, coord_quickmap,
#> coord_trans, cut_interval, cut_number, cut_width, discrete_scale,
#> draw_key_abline, draw_key_blank, draw_key_crossbar,
#> draw_key_dotplot, draw_key_label, draw_key_path, draw_key_point,
#> draw_key_pointrange, draw_key_polygon, draw_key_rect,
#> draw_key_smooth, draw_key_text, draw_key_vline, draw_key_vpath,
#> economics, economics_long, element_blank, element_grob,
#> element_line, element_rect, element_text, expand_limits,
#> facet_grid, facet_null, facet_wrap, fortify, geom_abline,
#> geom_area, geom_bar, geom_bin2d, geom_blank, geom_contour,
#> geom_count, geom_crossbar, geom_curve, geom_density,
#> geom_density2d, geom_density_2d, geom_dotplot, geom_errorbar,
#> geom_errorbarh, geom_freqpoly, geom_hex, geom_histogram,
#> geom_hline, geom_jitter, geom_label, geom_line, geom_linerange,
#> geom_map, geom_path, geom_point, geom_pointrange, geom_polygon,
#> geom_qq, geom_raster, geom_rect, geom_ribbon, geom_rug,
#> geom_segment, geom_smooth, geom_spoke, geom_step, geom_text,
#> geom_tile, geom_violin, geom_vline, gg_dep, ggplot, ggplotGrob,
#> ggplot_build, ggplot_gtable, ggsave, ggtitle, guide_colorbar,
#> guide_colourbar, guide_legend, guides, is.Coord, is.facet,
#> is.ggplot, is.theme, label_both, label_bquote, label_context,
#> label_parsed, label_value, label_wrap_gen, labeller, labs,
#> last_plot, layer, layer_data, layer_grob, layer_scales, lims,
#> map_data, margin, mean_cl_boot, mean_cl_normal, mean_sdl, mean_se,
#> median_hilow, position_dodge, position_fill, position_identity,
#> position_jitter, position_jitterdodge, position_nudge,
#> position_stack, presidential, qplot, quickplot, rel,
#> remove_missing, resolution, scale_alpha, scale_alpha_continuous,
#> scale_alpha_discrete, scale_alpha_identity, scale_alpha_manual,
#> scale_color_brewer, scale_color_continuous, scale_color_discrete,
#> scale_color_distiller, scale_color_gradient, scale_color_gradient2,
#> scale_color_gradientn, scale_color_grey, scale_color_hue,
#> scale_color_identity, scale_color_manual, scale_colour_brewer,
#> scale_colour_continuous, scale_colour_date, scale_colour_datetime,
#> scale_colour_discrete, scale_colour_distiller,
#> scale_colour_gradient, scale_colour_gradient2,
#> scale_colour_gradientn, scale_colour_grey, scale_colour_hue,
#> scale_colour_identity, scale_colour_manual, scale_fill_brewer,
#> scale_fill_continuous, scale_fill_date, scale_fill_datetime,
#> scale_fill_discrete, scale_fill_distiller, scale_fill_gradient,
#> scale_fill_gradient2, scale_fill_gradientn, scale_fill_grey,
#> scale_fill_hue, scale_fill_identity, scale_fill_manual,
#> scale_linetype, scale_linetype_continuous, scale_linetype_discrete,
#> scale_linetype_identity, scale_linetype_manual, scale_radius,
#> scale_shape, scale_shape_continuous, scale_shape_discrete,
#> scale_shape_identity, scale_shape_manual, scale_size,
#> scale_size_area, scale_size_continuous, scale_size_date,
#> scale_size_datetime, scale_size_discrete, scale_size_identity,
#> scale_size_manual, scale_x_continuous, scale_x_date,
#> scale_x_datetime, scale_x_discrete, scale_x_log10, scale_x_reverse,
#> scale_x_sqrt, scale_y_continuous, scale_y_date, scale_y_datetime,
#> scale_y_discrete, scale_y_log10, scale_y_reverse, scale_y_sqrt,
#> should_stop, stat_bin, stat_bin2d, stat_bin_2d, stat_bin_hex,
#> stat_binhex, stat_contour, stat_count, stat_density,
#> stat_density2d, stat_density_2d, stat_ecdf, stat_ellipse,
#> stat_function, stat_identity, stat_qq, stat_smooth, stat_spoke,
#> stat_sum, stat_summary, stat_summary2d, stat_summary_2d,
#> stat_summary_bin, stat_summary_hex, stat_unique, stat_ydensity,
#> theme, theme_bw, theme_classic, theme_dark, theme_get, theme_gray,
#> theme_grey, theme_light, theme_linedraw, theme_minimal,
#> theme_replace, theme_set, theme_update, theme_void,
#> transform_position, update_geom_defaults, update_labels,
#> update_stat_defaults, waiver, xlab, xlim, ylab, ylim, zeroGrob
In the simulated data above, we can see that
- for the easy pattern, it is the same for both people, so it should be possible/easy to train on one person, and accurately predict on another.
- for the impossible pattern, it is different for each person, so it should not be possible to train on one person, and accurately predict on another.
Benchmark: computing test error
In the code below, we define a K-fold cross-validation experiment.
(reg_same_other <- mlr3resampling::ResamplingSameOtherCV$new())
#> <ResamplingSameOtherCV> : Same versus Other Cross-Validation
#> * Iterations:
#> * Instantiated: FALSE
#> * Parameters:
#> List of 1
#> $ folds: int 3
In the code below, we define two learners to compare,
(reg.learner.list <- list(
if(requireNamespace("rpart"))mlr3::LearnerRegrRpart$new(),
mlr3::LearnerRegrFeatureless$new()))
#> [[1]]
#> <LearnerRegrRpart:regr.rpart>: Regression Tree
#> * Model: -
#> * Parameters: xval=0
#> * Packages: mlr3, rpart
#> * Predict Types: [response]
#> * Feature Types: logical, integer, numeric, factor, ordered
#> * Properties: importance, missings, selected_features, weights
#>
#> [[2]]
#> <LearnerRegrFeatureless:regr.featureless>: Featureless Regression Learner
#> * Model: -
#> * Parameters: robust=FALSE
#> * Packages: mlr3, stats
#> * Predict Types: [response], se
#> * Feature Types: logical, integer, numeric, character, factor, ordered,
#> POSIXct
#> * Properties: featureless, importance, missings, selected_features
In the code below, we define the benchmark grid, which is all combinations of tasks (easy and impossible), learners (rpart and featureless), and the one resampling method.
(reg.bench.grid <- mlr3::benchmark_grid(
reg.task.list,
reg.learner.list,
reg_same_other))
#> task learner resampling
#> <char> <char> <char>
#> 1: easy regr.rpart same_other_cv
#> 2: easy regr.featureless same_other_cv
#> 3: impossible regr.rpart same_other_cv
#> 4: impossible regr.featureless same_other_cv
In the code below, we execute the benchmark experiment (in parallel using the multisession future plan).
if(FALSE){#for CRAN.
if(require(future))plan("multisession")
}
if(require(lgr))get_logger("mlr3")$set_threshold("warn")
#> Le chargement a nécessité le package : lgr
#>
#> Attachement du package : 'lgr'
#> L'objet suivant est masqué depuis 'package:ggplot2':
#>
#> Layout
(reg.bench.result <- mlr3::benchmark(
reg.bench.grid, store_models = TRUE))
#> <BenchmarkResult> of 72 rows with 4 resampling runs
#> nr task_id learner_id resampling_id iters warnings errors
#> 1 easy regr.rpart same_other_cv 18 0 0
#> 2 easy regr.featureless same_other_cv 18 0 0
#> 3 impossible regr.rpart same_other_cv 18 0 0
#> 4 impossible regr.featureless same_other_cv 18 0 0
The code below computes the test error for each split,
reg.bench.score <- mlr3resampling::score(reg.bench.result)
reg.bench.score[1]
#> train.subsets test.fold test.subset person iteration test
#> <char> <int> <int> <int> <int> <list>
#> 1: all 1 1 1 1 1, 3, 5, 6,12,13,...
#> train uhash nr
#> <list> <char> <int>
#> 1: 4, 7, 9,10,18,20,... 7eb31050-513c-4ba8-8551-1616e5820596 1
#> task task_id learner learner_id
#> <list> <char> <list> <char>
#> 1: <TaskRegr:easy> easy <LearnerRegrRpart:regr.rpart> regr.rpart
#> resampling resampling_id prediction regr.mse algorithm
#> <list> <char> <list> <num> <char>
#> 1: <ResamplingSameOtherCV> same_other_cv <PredictionRegr> 1.638015 rpart
The code below visualizes the resulting test accuracy numbers.
if(require(animint2)){
ggplot()+
scale_x_log10()+
geom_point(aes(
regr.mse, train.subsets, color=algorithm),
shape=1,
data=reg.bench.score)+
facet_grid(
task_id ~ person,
labeller=label_both,
scales="free")
}
It is clear from the plot above that
- for the easy task, training on same is just as good as all or other subsets. rpart has much lower test error than featureless, in all three train subsets.
- for the impossible task, the least test error is using rpart with same train subsets; featureless with same train subsets is next best; training on all is substantially worse (for both featureless and rpart); training on other is even worse (patterns in the two people are completely different).
- in a real data task, training on other will most likely not be quite as bad as in the impossible task above, but also not as good as in the easy task.
Interactive visualization of data, test error, and splits
The code below can be used to create an interactive data visualization which allows exploring how different functions are learned during different splits.
inst <- reg.bench.score$resampling[[1]]$instance
rect.expand <- 0.2
grid.dt <- data.table(x=seq(-abs.x, abs.x, l=101), y=0)
grid.task <- mlr3::TaskRegr$new("grid", grid.dt, target="y")
pred.dt.list <- list()
point.dt.list <- list()
for(score.i in 1:nrow(reg.bench.score)){
reg.bench.row <- reg.bench.score[score.i]
task.dt <- data.table(
reg.bench.row$task[[1]]$data(),
reg.bench.row$resampling[[1]]$instance$id.dt)
names(task.dt)[1] <- "y"
set.ids <- data.table(
set.name=c("test","train")
)[
, data.table(row_id=reg.bench.row[[set.name]][[1]])
, by=set.name]
i.points <- set.ids[
task.dt, on="row_id"
][
is.na(set.name), set.name := "unused"
]
point.dt.list[[score.i]] <- data.table(
reg.bench.row[, .(task_id, iteration)],
i.points)
i.learner <- reg.bench.row$learner[[1]]
pred.dt.list[[score.i]] <- data.table(
reg.bench.row[, .(
task_id, iteration, algorithm
)],
as.data.table(
i.learner$predict(grid.task)
)[, .(x=grid.dt$x, y=response)]
)
}
(pred.dt <- rbindlist(pred.dt.list))
#> task_id iteration algorithm x y
#> <char> <int> <char> <num> <num>
#> 1: easy 1 rpart -2.00 3.557968
#> 2: easy 1 rpart -1.96 3.557968
#> 3: easy 1 rpart -1.92 3.557968
#> 4: easy 1 rpart -1.88 3.557968
#> 5: easy 1 rpart -1.84 3.557968
#> ---
#> 7268: impossible 18 featureless 1.84 7.204232
#> 7269: impossible 18 featureless 1.88 7.204232
#> 7270: impossible 18 featureless 1.92 7.204232
#> 7271: impossible 18 featureless 1.96 7.204232
#> 7272: impossible 18 featureless 2.00 7.204232
(point.dt <- rbindlist(point.dt.list))
#> task_id iteration set.name row_id y x fold person
#> <char> <int> <char> <int> <num> <num> <int> <int>
#> 1: easy 1 test 1 1.32996609 -0.9379653 1 1
#> 2: easy 1 train 2 0.24307692 -0.5115044 3 1
#> 3: easy 1 test 3 -0.23314657 0.2914135 1 1
#> 4: easy 1 train 4 1.73677545 1.6328312 2 1
#> 5: easy 1 test 5 -0.06356159 -1.1932723 1 1
#> ---
#> 21596: impossible 18 train 296 5.18094849 0.7257701 1 2
#> 21597: impossible 18 train 297 9.60431191 -1.6033236 1 2
#> 21598: impossible 18 test 298 7.51198770 -1.5243898 3 2
#> 21599: impossible 18 train 299 11.03539747 -1.7982414 1 2
#> 21600: impossible 18 test 300 10.71968480 1.7170157 3 2
#> subset display_row
#> <int> <int>
#> 1: 1 1
#> 2: 1 101
#> 3: 1 2
#> 4: 1 51
#> 5: 1 3
#> ---
#> 21596: 2 198
#> 21597: 2 199
#> 21598: 2 299
#> 21599: 2 200
#> 21600: 2 300
set.colors <- c(
train="#1B9E77",
test="#D95F02",
unused="white")
algo.colors <- c(
featureless="blue",
rpart="red")
make_person_subset <- function(DT){
DT[, "person/subset" := person]
}
make_person_subset(point.dt)
make_person_subset(reg.bench.score)
if(require(animint2)){
viz <- animint(
title="Train/predict on subsets, regression",
pred=ggplot()+
ggtitle("Predictions for selected train/test split")+
theme_animint(height=400)+
scale_fill_manual(values=set.colors)+
geom_point(aes(
x, y, fill=set.name),
showSelected="iteration",
size=3,
shape=21,
data=point.dt)+
scale_color_manual(values=algo.colors)+
geom_line(aes(
x, y, color=algorithm, subset=paste(algorithm, iteration)),
showSelected="iteration",
data=pred.dt)+
facet_grid(
task_id ~ `person/subset`,
labeller=label_both,
space="free",
scales="free")+
scale_y_continuous(
breaks=seq(-100, 100, by=2)),
err=ggplot()+
ggtitle("Test error for each split")+
theme_animint(height=400)+
scale_y_log10(
"Mean squared error on test set")+
scale_fill_manual(values=algo.colors)+
scale_x_discrete(
"People/subsets in train set")+
geom_point(aes(
train.subsets, regr.mse, fill=algorithm),
shape=1,
size=5,
stroke=2,
color="black",
color_off=NA,
clickSelects="iteration",
data=reg.bench.score)+
facet_grid(
task_id ~ `person/subset`,
labeller=label_both,
scales="free"),
diagram=ggplot()+
ggtitle("Select train/test split")+
theme_bw()+
theme_animint(height=300)+
facet_grid(
. ~ train.subsets,
scales="free",
space="free")+
scale_size_manual(values=c(subset=3, fold=1))+
scale_color_manual(values=c(subset="orange", fold="grey50"))+
geom_rect(aes(
xmin=-Inf, xmax=Inf,
color=rows,
size=rows,
ymin=display_row, ymax=display_end),
fill=NA,
data=inst$viz.rect.dt)+
scale_fill_manual(values=set.colors)+
geom_rect(aes(
xmin=iteration-rect.expand, ymin=display_row,
xmax=iteration+rect.expand, ymax=display_end,
fill=set.name),
clickSelects="iteration",
data=inst$viz.set.dt)+
geom_text(aes(
ifelse(rows=="subset", Inf, -Inf),
(display_row+display_end)/2,
hjust=ifelse(rows=="subset", 1, 0),
label=paste0(rows, "=", ifelse(rows=="subset", subset, fold))),
data=data.table(train.name="same", inst$viz.rect.dt))+
scale_x_continuous(
"Split number / cross-validation iteration")+
scale_y_continuous(
"Row number"),
source="https://github.com/tdhock/mlr3resampling/blob/main/vignettes/ResamplingSameOtherCV.Rmd")
viz
}
| , and |