The goal of kerntools is to provide R tools for working with a family
of Machine Learning methods called kernel methods. It can be used to
complement other R packages like kernlab
. Right now,
kerntools
implements several kernel functions for treating
non-negative and real vectors, real matrices, categorical and ordinal
variables, sets, and strings. Several tools for studying the resulting
kernel matrix or to compare two kernel matrices are available. These
diagnostic tools may be used to infer the kernel(s) matrix(ces)
suitability in training models. This package also provides functions for
computing the feature importance of Support Vector Machines (SVMs)
models, and display customizable kernel Principal Components Analysis
(PCA) plots. For convenience, widespread performance measures and
feature importance barplots are available for the user.
Installing kerntools
is easy. In the R console:
install.packages("kerntools")
Once the package is installed, it can be loaded anytime typing:
library(kerntools)
kerntools
requires R (>= 2.10). Currently, it also
relies on the following packages:
dplyr
ggplot2
kernlab
methods
reshape2
stringi
Usually, if some of these packages are missing in your library, they
will be installed automatically when kerntools
is
installed.
Imagine that you want to perform a (kernel) PCA plot but your dataset
consist of categorical variables. This can be done very easily with
kerntools
! First, you chose an appropriate kernel for your
data (in this example, the Dirac kernel for categorical variables), and
then you pass the output of the Dirac()
function to the
kPCA()
function.
head(showdata)
#> Favorite.color Favorite.actress Favorite.actor Favorite.show
#> 1 red Sophie Turner Josh O'Connor The crown
#> 2 black Soo Ye-jin Hyun Bin Bridgerton
#> 3 red Lorraine Ashbourne Henry Cavill Bridgerton
#> 4 blue Sophie Turner Alvaro Morte La casa de papel
#> 5 red Sophie Turner Michael K Williams The wire
#> 6 yellow Sophie Turner Kit Harington Game of Thrones
#> Liked.new.show
#> 1 Yes
#> 2 No
#> 3 Yes
#> 4 No
#> 5 Yes
#> 6 No
<- Dirac(showdata[,1:4])
KD <- kPCA(KD,plot=c(1,2),title="Survey", name_leg = "Liked the new
dirac_kpca show?", y=showdata$Liked.new.show, ellipse=0.66)
$plot dirac_kpca
You can customize your kernel PCA plot: apart from picking which
principal components you want to display (in the example: PC1 and PC2),
you may want to add a title, or a legend, or use different colors to
represent an additional variable of interest, so you can check patterns
on your data. To see in detail how to customize a kPCA()
plot, please refer to the documentation. The projection matrix is also
returned (dirac_kpca$projection
), so you may use it for
further analyses and/or creating your own plot.
Right now, kerntools
can deal effortlessly with the
following kinds of data:
Several tools for visualizing and comparing kernel matrices are provided.
Regarding kernel PCA, kerntools
allows the user to:
kerntools
or provided by the user.When using some specific kernels, kerntools
computes the
importance of each variable or feature in a Support Vector Machine (SVM)
model. kerntools
does not train SVMs or other prediction
models, but it can recover the feature importance of models fitted with
other packages (for instance kernlab
). These importances
can be sorted and summarized in a customizable barplot.
Finally, the following performance measures for regression, binary and multi-class classification are implemented:
kerntools
contains a categorical toy dataset called
showdata
.
To see detailed and step-by-step examples that illustrate the main
cases of use of kerntools
, please have a look to the
vignette:
browseVignettes(kerntools)
Remember that detailed, argument-by-argument documentation is available for each function:
help(kPCA) ## or the specific name of the function
?kPCA
The documentation of the example dataset is available in an analogous way, typing:
help(showdata)
?showdata
To know more about kernel functions, matrices and methods, you can consult the following reference materials:
Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). Chapter 6, pp. 291-323. New York: springer.
Müller, K. R., Mika, S., Tsuda, K., & Schölkopf, K. (2018) An introduction to kernel-based learning algorithms. In Handbook of neural network signal processing (pp. 4-1). CRC Press.
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge university press.