Update of Readme regarding migration

lalvim · Oct 15, 2020 · 658d9cb · 658d9cb
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 # PLSRegressor.jl
 [![][travis-img]][travis-url] [![][codecov-img]][codecov-url] [![][coverage-img]][coverage-url]
 
-The PLSRegressor.jl package is a package with Partial Least Squares Regressor methods. Contains PLS1, PLS2 and Kernel PLS2 NIPALS algorithms.
-Can be used mainly for regression. However, for classification task, binarizing targets and then obtaining multiple targets, you can apply KPLS.
+The PLSRegressor.jl is deprecated. It has moved to another package with a better interface that uses MLJ.
+We do not recommend use this package anymore. Use the new one with the link below.
 
-## Install
+## Migrated
 
-The package can be installed with the Julia package manager.
-From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
+We invite you to look at: https://github.com/lalvim/PartialLeastSquaresRegressor.jl
 
-```
-pkg> add PLSRegressor
-```
-
-Or, equivalently, via the `Pkg` API:
-
-```julia
-julia> import Pkg; Pkg.add("PLSRegressor")
-```
-
-## Using
-
-PLSRegressor is compatible with [MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine learning framework. Here are a few examples to show the Package functionalities:
-
-### Example 1
-
-```julia
-using MLJBase, RDatasets, MLJTuning
-@load KPLS pkg=PLSRegressor
-
-# loading data and selecting some features
-data = dataset("datasets", "longley")[:, 2:5]
-
-# unpacking the target
-y, X = unpack(data, ==(:GNP), colname -> true)
-
-# loading the model
-pls_model = KPLS()
-
-# defining hyperparams for tunning
-r1 = range(pls_model, :width, lower=0.001, upper=100.0, scale=:log)
-r2 = range(pls_model, :n_factors, lower=1, upper=3)
-
-# attaching tune
-self_tuning_pls_model = TunedModel(model = pls_model,
-                                   resampling = CV(nfolds = 10),
-                                   tuning = Grid(resolution = 100),
-                                   range = [r1, r2],
-                                   measure = mae)
-
-# putting into the machine
-self_tuning_pls = machine(self_tuning_pls_model, X, y)
-
-# fitting with tunning
-fit!(self_tuning_pls, verbosity=0)
-
-# getting the reports
-report(self_tuning_pls).best_result
-report(self_tuning_pls).best_model
-```
-
-### Example 2
-
-```julia
-using MLJBase, RDatasets
-@load PLS pkg=PLSRegressor
-
-# loading data and selecting some features
-data = dataset("datasets", "longley")[:, 2:5]
-
-# unpacking the target
-y, X = unpack(data, ==(:GNP), colname -> true)
-
-# loading the model
-regressor = PLS(n_factors=2)
-
-# building a pipeline with scaling on data
-pls_model = @pipeline Standardizer regressor target=Standardizer
-
-# a simple hould out
-train, test = partition(eachindex(y), 0.7, shuffle=true)
-
-pls_machine = machine(pls_model, X, y)
-
-fit!(pls_machine, rows=train)
-
-yhat = predict(pls_machine, rows=test)
-
-mae(yhat, y[test]) |> mean
-```
-
-## What is Implemented
-
-* A fast linear algorithm for single targets (PLS1 - NIPALS)
-* A linear algorithm for multiple targets (PLS2 - NIPALS)
-* A non linear algorithm for multiple targets (Kernel PLS2 - NIPALS)
-
-## Model Description
-
-* PLS - PLS MLJ model (PLS1 or PLS2)
-    * n_factors::Int = 10 - The number of latent variables to explain the data.
-
-* KPLS - Kernel PLS MLJ model
-    * nfactors::Int = 10 - The number of latent variables to explain the data.
-    * kernel::AbstractString = "rbf" - use a non linear kernel.
-    * width::AbstractFloat   = 1.0 - If you want to z-score columns. Recommended if not z-scored yet.
-
-## References
-
-* PLS1 and PLS2 based on
-   * Bob Collins Slides, LPAC Group. http://vision.cse.psu.edu/seminars/talks/PLSpresentation.pdf
-* A Kernel PLS2 based on
-   * Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space" by Roman Rosipal and Leonard J Trejo. Journal of Machine Learning Research 2 (2001) 97-123 http://www.jmlr.org/papers/volume2/rosipal01a/rosipal01a.pdf
-
-* NIPALS: Nonlinear Iterative Partial Least Squares
-    * Wold, H. (1966). Estimation of principal components and related models
-by iterative least squares. In P.R. Krishnaiaah (Ed.). Multivariate Analysis.
-(pp.391-420) New York: Academic Press.
-
-* SIMPLS: more efficient, optimal result
-    * Supports multivariate Y
-    * De Jong, S., 1993. SIMPLS: an alternative approach to partial least squares
-regression. Chemometrics and Intelligent Laboratory Systems, 18: 251–
-263
-
-## License
-
-The PLSRegressor.jl is free software: you can redistribute it and/or modify it under the terms of the MIT "Expat"
-License. A copy of this license is provided in ``LICENSE``
 
 [travis-img]: https://travis-ci.com/lalvim/PLSRegressor.jl.svg?branch=master
 [travis-url]: https://travis-ci.com/lalvim/PLSRegressor.jl