
{SLmetrics} is a
lightweight R package written in C++ and {Rcpp} for
memory-efficient and lightning-fast machine learning
performance evaluation; it’s like using a supercharged {yardstick} but
without the risk of soft to super-hard deprecations. {SLmetrics} covers both
regression and classification metrics and provides (almost) the same
array of metrics as {scikit-learn}
and {PyTorch} all
without {reticulate}
and the Python compile-run-(crash)-debug cycle.
Depending on the mood and alignment of planets {SLmetrics} stands for
Supervised Learning metrics, or Statistical Learning metrics. If {SLmetrics} catches on,
the latter will be the core philosophy and include unsupervised learning
metrics. If not, then it will remain a {pkg} for Supervised Learning
metrics, and a sandbox for me to develop my C++ skills.
Below you’ll find instructions to install {SLmetrics} and get started with your first metric, the Root Mean Squared Error (RMSE).
## install latest CRAN build
install.packages("SLmetrics")Below is a minimal example demonstrating how to compute both unweighted and weighted RMSE.
library(SLmetrics)
actual    <- c(10.2, 12.5, 14.1)
predicted <- c(9.8, 11.5, 14.2)
weights   <- c(0.2, 0.5, 0.3)
cat(
  "Root Mean Squared Error", rmse(
    actual    = actual,
    predicted = predicted,
  ),
  "Root Mean Squared Error (weighted)", weighted.rmse(
    actual    = actual,
    predicted = predicted,
    w         = weights
  ),
  sep = "\n"
)
#> Root Mean Squared Error
#> 0.6244998
#> Root Mean Squared Error (weighted)
#> 0.7314369That’s all! Now you can explore the rest of this README for in-depth usage, performance comparisons, and more details about {SLmetrics}.
Machine learning can be a complicated task; the steps from feature engineering to model deployment require carefully measured actions and decisions. One low-hanging fruit to simplify this process is performance evaluation.
At its core, performance evaluation is essentially just comparing two vectors - a programmatically and, at times, mathematically trivial step in the machine learning pipeline, but one that can become complicated due to:
{SLmetrics} solves these issues by being:
C++ and {Rcpp}Performance evaluation should be plug-and-play and “just work” out of the box - there’s no need to worry about quasiquations, dependencies, deprecations, or variations of the same functions relative to their arguments when using {SLmetrics}.
One, obviously, can’t build an R-package on
C++ and {Rcpp} without a proper
pissing contest at the urinals - below is a comparison in execution time
and memory efficiency of two simple cases that any {pkg} should be able
to handle gracefully; computing a 2 x 2 confusion matrix and computing
the RMSE1.

As shown in the chart, {SLmetrics} maintains consistently low(er) execution times across different sample sizes.
Below are the results for garbage collections and total memory allocations when computing a 2×2 confusion matrix (N = 1e7) and RMSE (N = 1e7) 2. Notice that {SLmetrics} requires no GC calls for these operations.
| Iterations | Garbage Collections [gc()] | gc() pr. second | Memory Allocation (MB) | |
|---|---|---|---|---|
| {SLmetrics} | 100 | 0 | 0.00 | 0 | 
| {yardstick} | 100 | 190 | 4.44 | 381 | 
| {MLmetrics} | 100 | 186 | 4.50 | 381 | 
| {mlr3measures} | 100 | 371 | 3.93 | 916 | 
2 x 2 Confusion Matrix (N = 1e7)
| Iterations | Garbage Collections [gc()] | gc() pr. second | Memory Allocation (MB) | |
|---|---|---|---|---|
| {SLmetrics} | 100 | 0 | 0.00 | 0 | 
| {yardstick} | 100 | 149 | 4.30 | 420 | 
| {MLmetrics} | 100 | 15 | 2.00 | 76 | 
| {mlr3measures} | 100 | 12 | 1.29 | 76 | 
RMSE (N = 1e7)
In both tasks, {SLmetrics} remains extremely memory-efficient, even at large sample sizes.
[!IMPORTANT]
From {bench} documentation: Total amount of memory allocated by R while running the expression. Memory allocated outside the R heap, e.g. by
malloc()or new directly is not tracked, take care to avoid misinterpreting the results if running code that may do this.
In its simplest form, {SLmetrics}-functions
work directly with pairs of <numeric> vectors (for
regression) or <factor> vectors (for classification).
Below we demonstrate this on two well-known datasets,
mtcars (regression) and iris
(classification).
We first fit a linear model to predict mpg in the
mtcars dataset, then compute the in-sample RMSE:
## Evaluate a linear model on mpg (mtcars)
model <- lm(mpg ~ ., data = mtcars)
rmse(mtcars$mpg, fitted(model))
#> [1] 2.146905Now we recode the iris dataset into a binary problem
(“virginica” vs. “others”) and fit a logistic regression. Then we
generate predicted classes, compute the confusion matrix and summarize
it.
## 1) recode iris
## to binary problem
iris$species_num <- as.numeric(
  iris$Species == "virginica"
)
## 2) fit the logistic
## regression
model <- glm(
  formula = species_num ~ Sepal.Length + Sepal.Width,
  data    = iris,
  family  = binomial(
    link = "logit"
  )
)
## 3) generate predicted
## classes
predicted <- factor(
  as.numeric(
    predict(model, type = "response") > 0.5
  ),
  levels = c(1,0),
  labels = c("Virginica", "Others")
)
## 4) generate actual
## values as factor
actual <- factor(
  x = iris$species_num,
  levels = c(1,0),
  labels = c("Virginica", "Others")
)## 4) generate
## confusion matrix
summary(
  confusion_matrix <- cmatrix(
    actual    = actual,
    predicted = predicted
  )
)
#> Confusion Matrix (2 x 2) 
#> ================================================================================
#>           Virginica Others
#> Virginica        35     15
#> Others           14     86
#> ================================================================================
#> Overall Statistics (micro average)
#>  - Accuracy:          0.81
#>  - Balanced Accuracy: 0.78
#>  - Sensitivity:       0.81
#>  - Specificity:       0.81
#>  - Precision:         0.81[!IMPORTANT]
OpenMP support in {SLmetrics} is experimental. Use it with caution, as performance gains and stability may vary based on your system configuration and workload.
You can control OpenMP usage within {SLmetrics} using
openmp.on() and openmp.off() . Below are
examples demonstrating how to enable and disable OpenMP:
## enable OpenMP
SLmetrics::openmp.on()
#> OpenMP enabled!
## disable OpenMP
SLmetrics::openmp.off()
#> OpenMP disabled!To illustrate the impact of OpenMP on performance, consider the following benchmarks for calculating entropy on a 1,000,000 x 200 matrix over 100 iterations3.
| Iterations | Runtime (sec) | Garbage Collections [gc()] | gc() pr. second | Memory Allocation (MB) | 
|---|---|---|---|---|
| 100 | 0.86 | 0 | 0 | 0 | 
1e6 x 200 matrix without OpenMP
| Iterations | Runtime (sec) | Garbage Collections [gc()] | gc() pr. second | Memory Allocation (MB) | 
|---|---|---|---|---|
| 100 | 0.15 | 0 | 0 | 0 | 
1e6 x 200 matrix with OpenMP
## install github release
pak::pak(
    pkg = "serkor1/SLmetrics@*release",
    ask = FALSE
)git clone --recurse-submodules https://github.com/serkor1/SLmetrics.gitmake build## install nightly build
pak::pak(
    pkg = ".",
    ask = FALSE
)Please note that the {SLmetrics} project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.