Dear Scikit-Learn contributor,
We are doing research on understanding how developers manage a special kind
of Technical Debt in *Python.*
We kindly ask 15-20 minutes of your time to fill out our survey. To help
you decide whether to fill it in, we clarify two points.
“Why should I answer this survey?”
Your participation is essential for us to correctly understand how
developers manage Technical Debt.
“What is in it for me?”
Your valuable contributions to *Scikit-Learn* are part of the information
we analyzed for this study. Thus, if you help us further by answering
this survey, there are two immediate benefits:
- you help to improve the efficiency of maintaining the quality of
*Scikit-Learn*.
- the results will be used to propose recommendations to manage
technical debt and create tool support.
Here is the link to the survey
<https://docs.google.com/forms/d/e/1FAIpQLSc-L24rO0W2eicLw5xxpSyg2MqXuunhxM8…>
.
Thank you for your time and attention.
Kind regards,
Jie Tan, Daniel Feitosa and Paris Avgeriou
Software Engineering and Architecture group <http://www.cs.rug.nl/search>
Faculty of Science and Engineering
University of Groningen, the Netherlands
Dear Maintainers,
I work on sparse PLS from now many years (doc+postDoc in INRIA and INSERM, see [ https://hadrienlorenzo.netlify.app/ | https://hadrienlorenzo.netlify.app ] for light view) and published about applications. Main problems are about dealing with missing values in the multi-output and degenerate n<<p contexts for multi-block structures.
I wrote packages
* in R : [ https://cran.r-project.org/web/packages/ddsPLS/index.html | https://cran.r-project.org/web/packages/ddsPLS/index.html ] ,
* and in Python : [ https://pypi.org/project/py-ddspls/ | https://pypi.org/project/py-ddspls/ ] .
In the both objectives of offering sparse PLS opportunities to the Python community and to improve my Python skills, I would like to propose you a python version of the algorithm for which specificities are the following.
* Modification of the PLS2 algorithm.
* Soft-thresholding of the empirical covariance matrices.
* Automatic-running of the number of component and the the sparsity parameter through bootstrap sampling.
* Sparsity both in X and in Y with a single parameter.
This algorithm has been developed with Jérôme Sarraco (Pr INRIA) and Rodolphe Thiébaut (PUPH Inserm) and recently with Olivier Cloarec (Sartorius, Research Group for Chemometrics, Institute of Chemistry, Umeå University, Umeå, S-901 87, Suède)
Would you be interested in this sparse version of the PLS algorithm ? I am more than eager to discuss about this project with you, so do not hesitate to contact me.
Bests,
Hadrien Lorenzo
+33 6 49 09 55 78
