Multi (& Mono) Data-Driven Sparse PLS

mddspls is the python light package of the data-driven sparse PLS algorithm

In the high dimensional settings (large number of variables), one objective is to select the relevant variables and thus to reduce the dimension. That subspace selection is often managed with supervised tools. However, some data can be missing, compromising the validity of the sub-space selection. We propose a PLS, Partial Least Square, based method, called dd-sPLS'' for data-driven-sparse PLS, and its multi-block version mdd-sPLS'' for multi-block-data-driven-sparse PLS, allowing jointly variable selection and subspace estimation while training and testing missing data imputation through a new algorithm called Koh-Lanta.

It contains one main class mddspls and one associated important method denote predict permitting to predict from a new dataset. The function called perf_mddsPLS permits to compute cross-validation.