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README.Rmd
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README.Rmd
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# Performance is not enough: the story told by a Rashomon's quartet
This repository contains the code to reproduce the data, models, and visualizations described in the paper:
P. Biecek, H. Baniecki, M. Krzyziński, D. Cook. *Performance is not enough: the story told by a Rashomon’s quartet*. Preprint [arXiv:2302.13356v2](https://arxiv.org/abs/2302.13356), 2023.
See also an R file [replicate.r](replicate.r)
## Read data
```r
train <- read.table("rq_train.csv", sep=";", header=TRUE)
test <- read.table("rq_test.csv", sep=";", header=TRUE)
```
## Train models
```r
set.seed(1568)
library(DALEX)
library(partykit)
model_dt <- ctree(y~., data = train, control = ctree_control(maxdepth = 3, minsplit = 250))
exp_dt <- DALEX::explain(model_dt, data = test[,-1], y = test[,1],
verbose = FALSE, label="decision tree")
mp_dt <- model_performance(exp_dt)
imp_dt <- model_parts(exp_dt, N=NULL, B=1, type = "difference")
model_lm <- lm(y~., data = train)
exp_lm <- DALEX::explain(model_lm, data = test[,-1], y = test[,1],
verbose = FALSE, label="linear regression")
mp_lm <- model_performance(exp_lm)
imp_lm <- model_parts(exp_lm, N=NULL, B=1, type = "difference")
library(randomForest)
model_rf <- randomForest(y~., data = train, ntree = 100)
exp_rf <- DALEX::explain(model_rf, data = test[,-1], y = test[,1],
verbose = FALSE, label="random forest")
mp_rf <- model_performance(exp_rf)
imp_rf <- model_parts(exp_rf, N=NULL, B=1, type = "difference")
library(neuralnet)
model_nn <- neuralnet(y~., data = train, hidden=c(8, 4), threshold=0.05)
exp_nn <- DALEX::explain(model_nn, data = test[,-1], y = test[,1],
verbose = FALSE, label="neural network")
mp_nn <- model_performance(exp_nn)
imp_nn <- model_parts(exp_nn, N=NULL, B=1, type = "difference")
# save binary versions just in case
save(exp_nn, exp_dt, exp_rf, exp_lm, file="models.RData")
```
## Let's see performance
```r
mp_all <- list(lm = mp_lm, dt = mp_dt, nn = mp_nn, rf = mp_rf)
R2 <- sapply(mp_all, function(x) x$measures$r2)
round(R2, 4)
# lm dt nn rf
# 0.7290 0.7287 0.7290 0.7287
rmse <- sapply(mp_all, function(x) x$measures$rmse)
round(rmse, 4)
# lm dt nn rf
# 0.3535 0.3537 0.3535 0.3537
```
## Let's see raw models
```r
plot(model_dt)
summary(model_lm)
model_rf
plot(model_nn)
```
<img width=600 src="figures/rq_plot_dt.png">
```
-------- LM
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.01268 0.01114 -1.138 0.255
x1 0.48481 0.03001 16.157 < 2e-16 ***
x2 0.14316 0.02966 4.826 1.61e-06 ***
x3 -0.03113 0.02980 -1.045 0.296
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.352 on 996 degrees of freedom
Multiple R-squared: 0.7268, Adjusted R-squared: 0.726
F-statistic: 883.4 on 3 and 996 DF, p-value: < 2.2e-16
-------- RF
randomForest(formula = y ~ ., data = train, ntree = 100)
Type of random forest: regression
Number of trees: 100
No. of variables tried at each split: 1
Mean of squared residuals: 0.1182976
% Var explained: 73.81
```
<img width=600 src="figures/rq_plot_nn.png">
## Variable importance
```r
plot(imp_dt, imp_nn, imp_rf, imp_lm)
```
<img width=600 src="figures/rq_plot_vip.png">
## Plot models
```r
pd_dt <- model_profile(exp_dt, N=NULL)
pd_rf <- model_profile(exp_rf, N=NULL)
pd_lm <- model_profile(exp_lm, N=NULL)
pd_nn <- model_profile(exp_nn, N=NULL)
plot(pd_dt, pd_nn, pd_rf, pd_lm)
```
<img width=600 src="figures/rq_plot_pd.png">
## Plot data distribution
```r
library("GGally")
both <- rbind(data.frame(train, label="train"),
data.frame(test, label="test"))
ggpairs(both, aes(color=label),
lower = list(continuous = wrap("points", alpha=0.2, size=1),
combo = wrap("facethist", bins=25)),
diag = list(continuous = wrap("densityDiag", alpha=0.5, bw="SJ"),
discrete = "barDiag"),
upper = list(continuous = wrap("cor", stars=FALSE)))
```
## Analysis of model residuals
The parallel coordinate plot depicts ranges for residuals for different models, one range per observation ordered along the mean value. The second panel shows between model averages and standard deviations for residuals, one point per observation. Following panels show the dendrogram and PCA for residuals.
<img width=600 src="residuals.png">
## Session info
```
> devtools::session_info()
─ Session info ──────────────────────────────────────────────────────────────────
setting value
version R version 4.2.2 (2022-10-31)
os macOS Monterey 12.5.1
system aarch64, darwin20
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Warsaw
date 2023-02-24
rstudio 2022.12.0+353 Elsbeth Geranium (desktop)
pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ──────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0)
bookdown 0.32 2023-01-17 [1] CRAN (R 4.2.0)
cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0)
callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.0)
caret * 6.0-93 2022-08-09 [1] CRAN (R 4.2.0)
checkmate 2.1.0 2022-04-21 [1] CRAN (R 4.2.0)
class 7.3-20 2022-01-16 [1] CRAN (R 4.2.2)
cli 3.6.0 2023-01-09 [1] CRAN (R 4.2.0)
cluster 2.1.4 2022-08-22 [1] CRAN (R 4.2.2)
codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.2)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.0)
ctv 0.9-4 2022-11-06 [1] CRAN (R 4.2.0)
DALEX * 2.4.3 2023-01-15 [1] Github (ModelOriented/DALEX@478a19d)
data.table 1.14.6 2022-11-16 [1] CRAN (R 4.2.0)
deldir 1.0-6 2021-10-23 [1] CRAN (R 4.2.0)
devtools 2.4.5 2022-10-11 [1] CRAN (R 4.2.0)
digest 0.6.30 2022-10-18 [1] CRAN (R 4.2.0)
dplyr 1.1.0 2023-01-29 [1] CRAN (R 4.2.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
evaluate 0.18 2022-11-07 [1] CRAN (R 4.2.0)
fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0)
farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.0)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.0)
foreign 0.8-83 2022-09-28 [1] CRAN (R 4.2.2)
Formula * 1.2-4 2020-10-16 [1] CRAN (R 4.2.0)
fs 1.6.1 2023-02-06 [1] CRAN (R 4.2.0)
future 1.29.0 2022-11-06 [1] CRAN (R 4.2.0)
future.apply 1.10.0 2022-11-05 [1] CRAN (R 4.2.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0)
ggplot2 * 3.4.0 2022-11-04 [1] CRAN (R 4.2.0)
glmnet * 4.1-6 2022-11-27 [1] CRAN (R 4.2.0)
globals 0.16.2 2022-11-21 [1] CRAN (R 4.2.0)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
gower 1.0.1 2022-12-22 [1] CRAN (R 4.2.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
gtable 0.3.1 2022-09-01 [1] CRAN (R 4.2.0)
hardhat 1.2.0 2022-06-30 [1] CRAN (R 4.2.0)
Hmisc * 4.7-2 2022-11-18 [1] CRAN (R 4.2.0)
htmlTable 2.4.1 2022-07-07 [1] CRAN (R 4.2.0)
htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0)
htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0)
httpuv 1.6.6 2022-09-08 [1] CRAN (R 4.2.0)
ingredients 2.3.1 2023-01-15 [1] Github (ModelOriented/ingredients@a63c06c)
interp 1.1-3 2022-07-13 [1] CRAN (R 4.2.0)
inum 1.0-4 2021-04-12 [1] CRAN (R 4.2.0)
ipred 0.9-13 2022-06-02 [1] CRAN (R 4.2.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
jpeg 0.1-10 2022-11-29 [1] CRAN (R 4.2.0)
knitr 1.41 2022-11-18 [1] CRAN (R 4.2.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0)
lattice * 0.20-45 2021-09-22 [1] CRAN (R 4.2.2)
latticeExtra 0.6-30 2022-07-04 [1] CRAN (R 4.2.0)
lava 1.7.1 2023-01-06 [1] CRAN (R 4.2.0)
libcoin * 1.0-9 2021-09-27 [1] CRAN (R 4.2.0)
lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0)
listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0)
lubridate 1.9.0 2022-11-06 [1] CRAN (R 4.2.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
MASS * 7.3-58.1 2022-08-03 [1] CRAN (R 4.2.2)
Matrix * 1.5-3 2022-11-11 [1] CRAN (R 4.2.0)
MatrixModels 0.5-1 2022-09-11 [1] CRAN (R 4.2.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0)
ModelMetrics 1.2.2.2 2020-03-17 [1] CRAN (R 4.2.0)
multcomp 1.4-20 2022-08-07 [1] CRAN (R 4.2.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
mvtnorm * 1.1-3 2021-10-08 [1] CRAN (R 4.2.0)
neuralnet * 1.44.2 2019-02-07 [1] CRAN (R 4.2.0)
nlme 3.1-161 2022-12-15 [1] CRAN (R 4.2.0)
nnet 7.3-18 2022-09-28 [1] CRAN (R 4.2.2)
parallelly 1.32.1 2022-07-21 [1] CRAN (R 4.2.0)
partykit * 1.2-16 2022-06-20 [1] CRAN (R 4.2.0)
patchwork * 1.1.2 2022-08-19 [1] CRAN (R 4.2.0)
pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.0)
pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.2.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.0)
plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.0)
png 0.1-8 2022-11-29 [1] CRAN (R 4.2.0)
polspline 1.1.22 2022-11-23 [1] CRAN (R 4.2.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0)
pROC 1.18.0 2021-09-03 [1] CRAN (R 4.2.0)
processx 3.8.0 2022-10-26 [1] CRAN (R 4.2.0)
prodlim 2019.11.13 2019-11-17 [1] CRAN (R 4.2.0)
profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.0)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
ps 1.7.2 2022-10-26 [1] CRAN (R 4.2.0)
purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.0)
quantreg 5.94 2022-07-20 [1] CRAN (R 4.2.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
randomForest * 4.7-1.1 2022-05-23 [1] CRAN (R 4.2.0)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.2.0)
recipes 1.0.4 2023-01-11 [1] CRAN (R 4.2.0)
remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.2.0)
rlang 1.0.6 2022-09-24 [1] CRAN (R 4.2.0)
rmarkdown 2.18 2022-11-09 [1] CRAN (R 4.2.0)
rms * 6.3-0 2022-04-22 [1] CRAN (R 4.2.0)
rpart * 4.1.19 2022-10-21 [1] CRAN (R 4.2.0)
rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0)
sandwich 3.0-2 2022-06-15 [1] CRAN (R 4.2.0)
scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.2.0)
shiny 1.7.3 2022-10-25 [1] CRAN (R 4.2.0)
SparseM * 1.81 2021-02-18 [1] CRAN (R 4.2.0)
stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0)
stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.0)
survival * 3.4-0 2022-08-09 [1] CRAN (R 4.2.2)
TH.data 1.1-1 2022-04-26 [1] CRAN (R 4.2.0)
tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.0)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0)
timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.0)
timeDate 4022.108 2023-01-07 [1] CRAN (R 4.2.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0)
usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0)
vctrs 0.5.2 2023-01-23 [1] CRAN (R 4.2.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
xfun 0.37 2023-01-31 [1] CRAN (R 4.2.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0)
zoo 1.8-11 2022-09-17 [1] CRAN (R 4.2.0)
```