Improved Kernel Partial Least Squares (IKPLS) and Fast Cross-Validation

The ikpls software package provides fast and efficient tools for PLS (Partial Least Squares) modeling. This package is designed to help researchers and practitioners handle PLS modeling faster than previously possible - particularly on large datasets.

NEW IN 3.0.0: Fast cross-validation for weighted IKPLS.

The ikpls software package now directly depends on the cvmatrix software package [11] to implement the fast cross-validation by Engstrøm and Jensen [7]. cvmatrix extends the fast cross-validation algorithms to correctly handle the weighted cases. The extension includes support for all 16 (12 unique) combinations of weighted centering and weighted scaling for X and Y, increasing neither time nor space complexity.

NEW IN 2.0.0: Weighted IKPLS

The ikpls software package now also features sample-weighted PLS [8]. For this, ikpls uses the weighted mean [9] and standard deviation [10] as formulated by National Institute of Science and Technology (NIST). Both NumPy and JAX implementations allow for weighted cross-validation with their respective cross_validate methods.

Citation

If you use the ikpls software package for your work, please cite this Journal of Open Source Software article. If you use the fast cross-validation algorithm implemented in ikpls.fast_cross_validation.numpy_ikpls, please also cite this Journal of Chemometrics article.

Unlock the Power of Fast and Stable Partial Least Squares Modeling with IKPLS

Dive into cutting-edge Python implementations of the IKPLS (Improved Kernel Partial Least Squares) Algorithms #1 and #2 [1] for CPUs, GPUs, and TPUs. IKPLS is both fast [2] and numerically stable [3] making it optimal for PLS modeling.

• Use our NumPy [4] based CPU implementations for seamless integration with scikit-learn\'s [5] ecosystem of machine learning algorithms and pipelines. As the implementations subclass scikit-learn's BaseEstimator, they can be used with scikit-learn\'s cross_validate.
• Use our JAX [6] implementations on CPUs or leverage powerful GPUs and TPUs for PLS modelling. Our JAX implementations are end-to-end differentaible allowing gradient propagation when using PLS as a layer in a deep learning model.
• Use our combination of IKPLS with Engstrøm's unbelievably fast cross-validation algorithm [7] to quickly determine the optimal combination of preprocessing and number of PLS components.

The documentation is available at https://ikpls.readthedocs.io/en/latest/; examples can be found at https://github.com/Sm00thix/IKPLS/tree/main/examples.

Fast Cross-Validation

In addition to the standalone IKPLS implementations, this package contains an implementation of IKPLS combined with the novel, fast cross-validation algorithm by Engstrøm and Jensen [7]. The fast cross-validation algorithm benefit both IKPLS Algorithms and especially Algorithm #2. The fast cross-validation algorithm is mathematically equivalent to the classical cross-validation algorithm. Still, it is much quicker. The fast cross-validation algorithm correctly handles (column-wise) centering and scaling of the X and Y input matrices using training set means and standard deviations to avoid data leakage from the validation set. This centering and scaling can be enabled or disabled independently from eachother and for X and Y by setting the parameters center_X, center_Y, scale_X, and scale_Y, respectively. In addition to correctly handling (column-wise) centering and scaling, the fast cross-validation algorithm correctly handles row-wise preprocessing that operates independently on each sample such as (row-wise) centering and scaling of the X and Y input matrices, convolution, or other preprocessing. Row-wise preprocessing can safely be applied before passing the data to the fast cross-validation algorithm.

Prerequisites

The JAX implementations support running on both CPU, GPU, and TPU.

• To enable NVIDIA GPU execution, install JAX and CUDA with: shell pip3 install -U "jax[cuda12]"

• To enable Google Cloud TPU execution, install JAX with: shell pip3 install -U "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

These are typical installation instructions that will be what most users are looking for. For customized installations, follow the instructions from the JAX Installation Guide.

To ensure that JAX implementations use float64, set the environment variable JAXENABLEX64=True as per the Current Gotchas. Alternatively, float64 can be enabled with the following function call: python import jax jax.config.update("jax_enable_x64", True)

Installation

• Install the package for Python3 using the following command: shell pip3 install ikpls

• Now you can import the NumPy and JAX implementations with: python from ikpls.numpy_ikpls import PLS as NpPLS from ikpls.jax_ikpls_alg_1 import PLS as JAXPLS_Alg_1 from ikpls.jax_ikpls_alg_2 import PLS as JAXPLS_Alg_2 from ikpls.fast_cross_validation.numpy_ikpls import PLS as NpPLS_FastCV

Quick Start

Use the ikpls package for PLS modeling

```python import numpy as np

from ikpls.numpy_ikpls import PLS

N = 100 # Number of samples. K = 50 # Number of features. M = 10 # Number of targets. A = 20 # Number of latent variables (PLS components).

X = np.random.uniform(size=(N, K)) # Predictor variables Y = np.random.uniform(size=(N, M)) # Target variables w = np.random.uniform(size=(N, )) # sample weights

The other PLS algorithms and implementations have the same interface for fit()

and predict(). The fast cross-validation implementation with IKPLS has a

different interface.

npikplsalg1 = PLS(algorithm=1) npikplsalg1.fit(X, Y, A, w)

Has shape (A, N, M) = (20, 100, 10). Contains a prediction for all possible

numbers of components up to and including A.

ypred = npikplsalg1.predict(X)

Has shape (N, M) = (100, 10).

ypred20components = npikplsalg1.predict(X, ncomponents=20) (ypred20components == y_pred[19]).all() # True

The internal model parameters can be accessed as follows:

Regression coefficients tensor of shape (A, K, M) = (20, 50, 10).

npikplsalg_1.B

X weights matrix of shape (K, A) = (50, 20).

npikplsalg_1.W

X loadings matrix of shape (K, A) = (50, 20).

npikplsalg_1.P

Y loadings matrix of shape (M, A) = (10, 20).

npikplsalg_1.Q

X rotations matrix of shape (K, A) = (50, 20).

npikplsalg_1.R

X scores matrix of shape (N, A) = (100, 20).

This is only computed for IKPLS Algorithm #1.

npikplsalg_1.T ```

Examples

In examples, you will find:

• Fit and Predict with NumPy.
• Fit and Predict with JAX.
• Cross-validate with NumPy.
• Cross-validate with NumPy and scikit-learn.
• Cross-validate with NumPy and fast cross-validation.
• Cross-validate with NumPy and weighted fast cross-validation.
• Cross-validate with JAX.
• Compute the gradient of a preprocessing convolution filter with respect to the RMSE between the target value and the value predicted by PLS after fitting with JAX.
• Weighted Fit and Predict with NumPy.
• Weighted Fit and Predict with JAX.
• Weighted cross-validation with NumPy.
• Weighted cross-validation with JAX.

Contribute

To contribute, please read the Contribution Guidelines.

References

1. Dayal, B. S. and MacGregor, J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11(1), 73-85.
2. Alin, A. (2009). Comparison of PLS algorithms when the number of objects is much larger than the number of variables. Statistical Papers, 50, 711-720.
3. Andersson, M. (2009). A comparison of nine PLS1 algorithms. Journal of Chemometrics, 23(10), 518-529.
4. NumPy
5. scikit-learn
6. JAX
7. Engstrøm, O.-C. G. and Jensen, M. H. (2025). Fast Partition-Based Cross-Validation With Centering and Scaling for $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$
8. Becker and Ismail (2016). Accounting for sampling weights in PLS path modeling: Simulations and empirical examples. European Management Journal, 34(6), 606-617.
9. Weighted mean. National Institute of Standards and Technology.
10. Weighted standard deviation. National Institute of Standards and Technology.
11. CVMatrix. Fast computation of possibly weighted and possibly centered/scaled training set kernel matrices in a cross-validation setting.

Funding

• Up until May 31st 2025, this work has been carried out as part of an industrial Ph. D. project receiving funding from FOSS Analytical A/S and The Innovation Fund Denmark. Grant number 1044-00108B.
• From June 1st 2025 and onward, this work is sponsored by FOSS Analytical A/S.