direpack 0.9.0

A Python 3 Library for State-of-the-Art Statistical Dimension Reduction Methods

direpack: a Python 3 library for state-of-the-art statistical dimension reduction techniques

This package delivers a scikit-learn compatible Python 3 package for some state-of-the art multivariate statistical methods, with a focus on dimension reduction.

The categories of methods delivered in this package, are:

• Projection pursuit dimension reduction (ppdire folder; cf. ppdire Documentation file and ppdire Examples Notebook)
• Robust M-estimators for dimension reduction (sprm folder; cf. SPRM Documentation file and SPRM Examples Notebook)

The package also contains a set of tools for pre- and postprocessing:

• The preprocessing folder provides classical and robust centring and scaling, as well as spatial sign transforms [4]
• The dicomo folder contains a versatile class to access a wide variety of moment and co-moment statistics, and statistics derived from those. Check out the dicomo Examples Notebook.
• Plotting utilities in the plot folder
• Cross-validation utilities in the cross-validation folder

Methods in the sprm folder

• The estimator (sprm.py) [1]
• The Sparse NIPALS (SNIPLS) estimator [3](snipls.py)
• Robust M regression estimator (rm.py)
• Ancillary functions for M-estimation (_m_support_functions.py)

Methods in the ppdire folder

The ppdire class will give access to a wide range of projection pursuit dimension reduction techniques. These include slower approximate estimates for well-established methods such as PCA, PLS and continuum regression. However, the class provides unique access to a set of robust options, such as robust continuum regression (RCR) [5], through its native grid optimization algorithm, first published for RCR as well [6]. Moreover, ppdire is also a great gateway to calculate generalized betas, using the CAPI projection index [7].

The code is orghanized in

• ppdire.py - the main PP dimension reduction class
• capi.py - the co-moment analysis projection index.

How to install

The package is distributed through PyPI, so install through:

    pip install direpack

Documentation

Detailed documentation on how to use the classes is provided in the docs folder per class.

Examples

Jupyter Notebooks with Examples are provided for each of the classes in the examples folder.

References

1. Sparse partial robust M regression, Irene Hoffmann, Sven Serneels, Peter Filzmoser, Christophe Croux, Chemometrics and Intelligent Laboratory Systems, 149 (2015), 50-59.
2. Partial robust M regression, Sven Serneels, Christophe Croux, Peter Filzmoser, Pierre J. Van Espen, Chemometrics and Intelligent Laboratory Systems, 79 (2005), 55-64.
3. Sparse and robust PLS for binary classification, I. Hoffmann, P. Filzmoser, S. Serneels, K. Varmuza, Journal of Chemometrics, 30 (2016), 153-162.
4. Spatial Sign Preprocessing:  A Simple Way To Impart Moderate Robustness to Multivariate Estimators, Sven Serneels, Evert De Nolf, Pierre J. Van Espen, Journal of Chemical Information and Modeling, 46 (2006), 1402-1409.
5. Robust Continuum Regression, Sven Serneels, Peter Filzmoser, Christophe Croux, Pierre J. Van Espen, Chemometrics and Intelligent Laboratory Systems, 76 (2005), 197-204.
6. Robust Multivariate Methods: The Projection Pursuit Approach, Peter Filzmoser, Sven Serneels, Christophe Croux and Pierre J. Van Espen, in: From Data and Information Analysis to Knowledge Engineering, Spiliopoulou, M., Kruse, R., Borgelt, C., Nuernberger, A. and Gaul, W., eds., Springer Verlag, Berlin, Germany, 2006, pages 270--277.
7. Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis, Sven Serneels, in: JSM Proceedings, Business and Economic Statistics Section. Alexandria, VA: American Statistical Association, 2019, 3009-3035.

Release Notes can be checked out in the repository.

A list of possible topics for further development is provided as well. Additions and comments are welcome!