This is an open invitation to the community. PartialLeastSquaresRegressor.jl is a package made in Julia with the aim of solving regression problems, especially when there are few samples. Very suitable for domains such as biomedical, healthcare, chemistry or even other domains.
The package now has a new interface and is registered with MLJ. As soon as possible it will be possible to use it by calling via @load from MLJ.
If you want to help contribute to the package, welcome!
Below, a little bit of development history for those interested:
Three years ago I started the development of a package on an algorithm called Partial Least Squares Regressor very efficient for problems in which we have few samples. This was the first implementation, I believe, of this regressor in the Julia language. At the time, if I remember correctly, there was no implementation even in Scikit-learn and in R packages. During development, I implemented three versions of the algorithm: PLS1, a linear regressor with a single target; PLS2, a multitarget linear regressor; and finally, Kernel PLS, a multitarget regressor for non-linear problems. All of these in one package. Over time some colleagues helped us, mainly, @filipebraida.
Recently, the MLJ team (@ablaom , @tlienart ), and with help of @azev77, found us and helped us a lot so that we could put an interface for MLJ. Ah, we also changed the name of the package to obtain greater conciseness and clarity.
Hi. I Will create some issues in the repo. Some of the enhancements are: a) perform benchmarks for each algorithm and find ways to improve execution time b) implement Simpls c) automatic way of finding the factors using variance d) a nice documentation … e) be the best and fastest PLS package regarding other languages !!!
There’s actually a few other methods, which are not the same as PLSR called Partial Least Squares. Partial least squares - discriminant analysis (PLSDA), and partial least squares structural equation modelling(PLS-SEM). So the name is fine. Abbreviating to something like “PLSRegression.jl” could work.