{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 cylce.
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)
<- c(10.2, 12.5, 14.1)
actual <- c(9.8, 11.5, 14.2)
predicted <- c(0.2, 0.5, 0.3)
weights
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)
#> 0.7314369
That’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)
<- lm(mpg ~ ., data = mtcars)
model rmse(mtcars$mpg, fitted(model))
#> [1] 2.146905
Now 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
$species_num <- as.numeric(
iris$Species == "virginica"
iris
)
## 2) fit the logistic
## regression
<- glm(
model formula = species_num ~ Sepal.Length + Sepal.Width,
data = iris,
family = binomial(
link = "logit"
)
)
## 3) generate predicted
## classes
<- factor(
predicted as.numeric(
predict(model, type = "response") > 0.5
),levels = c(1,0),
labels = c("Virginica", "Others")
)
## 4) generate actual
## values as factor
<- factor(
actual x = iris$species_num,
levels = c(1,0),
labels = c("Virginica", "Others")
)
## 4) generate
## confusion matrix
summary(
<- cmatrix(
confusion_matrix 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 the setUseOpenMP function. Below are examples demonstrating how to enable and disable OpenMP:
## enable OpenMP
::openmp.on()
SLmetrics#> OpenMP enabled!
## disable OpenMP
::openmp.off()
SLmetrics#> 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(
pakpkg = "serkor1/SLmetrics@*release",
ask = FALSE
)
## install nightly build
::pak(
pakpkg = "serkor1/SLmetrics",
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.