diPLSlib 1.0.1

Python package for domain adaptation in multivariate regression

Domain-invariant partial least squares regression (di-PLS)

Python implementation of (m)di-PLS for domain adaptation in multivariate regression problems.

Installation

pip install diPLSlib

Usage

How to apply di-PLS

Train regression model

import dipals as ml

m = ml.model(X, y, X_source, X_target, 2)
l = [100000] #  Regularization
m.fit(l)

# Typically X=X_source and y are the corresponding response values

Apply the model

yhat_dipls, err = m.predict(X_test, y_test=[])

How to apply mdi-PLS

import dipals as ml

# Training
m = ml.model(X, y, X_source, X_target, 2)
l = [100000] #  Regularization
m.fit(l, target_domain=2)

# Testing
yhat_dipls, err = m.predict(X_test, y_test=[])


# X_target = [X1, X2, ... , Xk] is a list of target domain data
# The parameter target_domain specifies for which domain the model should be trained (here X2).

Acknowledgements

The first version of di-PLS was developed by Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, Bernhard Moser and Susanne Saminger-Platz and published in:

• Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, and Susanne Saminger-Platz Analytical Chemistry 2018 90 (11), 6693-6701 https://doi.org/10.1021/acs.analchem.8b00498

Further refinements to the initial algorithm were published in:

• R. Nikzad-Langerodi, W. Zellinger, S. Saminger-Platz and B. Moser, "Domain-Invariant Regression Under Beer-Lambert's Law," 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 581-586, https://doi.org/10.1109/ICMLA.2019.00108.

• Ramin Nikzad-Langerodi, Werner Zellinger, Susanne Saminger-Platz, Bernhard A. Moser, Domain adaptation for regression under Beer–Lambert’s law, Knowledge-Based Systems, Volume 210, 2020, https://doi.org/10.1016/j.knosys.2020.106447.

• Bianca Mikulasek, Valeria Fonseca Diaz, David Gabauer, Christoph Herwig, Ramin Nikzad-Langerodi, Partial least squares regression with multiple domains, J. Chemometrics, 2023 (to appear), https://doi.org/10.13140/RG.2.2.23750.75845

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