IKPLS
The facts below are what the maker publishes about IKPLS, checked and dated. Our editors have not written about it yet.
The facts
Each one read from IKPLS's own pages, with the date it was checked.
What it does
- Algorithms
- It implements Improved Kernel PLS Algorithms 1 and 2 for CPU, GPU, and TPU use.checked 12 Oct 2026
- Audience
- The project describes its intended users as researchers and practitioners doing PLS modeling.checked 12 Oct 2026
- Compute
- Its NumPy implementations run on CPUs, and its JAX implementations support CPUs, GPUs, and TPUs.checked 12 Oct 2026
- Deployment
- desktopchecked 23 Sep 2026
- Differentiability
- The JAX implementations are end-to-end differentiable, allowing gradient propagation when PLS is used as a layer in a deep learning model.checked 12 Oct 2026
- Documentation and examples
- The project provides online documentation and examples covering fitting, prediction, and cross-validation with NumPy, JAX, and scikit-learn.checked 12 Oct 2026
- Fast cross-validation
- The package includes a fast cross-validation algorithm described as mathematically equivalent to classical cross-validation and quicker to run.checked 12 Oct 2026
- Installation
- The package installs with pip using `pip3 install ikpls`; JAX implementations require the optional `jax` extra.checked 12 Oct 2026
- Installation requirement
- PyPI lists the requirement as Python >=3.10 and <3.15.checked 12 Oct 2026
- JAX
- Optional JAX implementations run on CPU, GPU, and TPU and support end-to-end differentiation.checked 12 Oct 2026
- License
- PyPI lists the package under the Apache-2.0 license.checked 12 Oct 2026
- NumPy and scikit-learn
- The NumPy CPU implementation includes a scikit-learn-conformant PLS wrapper that works with scikit-learn pipelines and cross-validation.checked 12 Oct 2026
- Preprocessing
- Fast cross-validation handles column-wise centering and scaling using training-set statistics to avoid validation-set data leakage, as well as sample-wise preprocessing such as convolution.checked 12 Oct 2026
- Purpose
- IKPLS is a Python package for fast partial least squares modeling, aimed at researchers and practitioners, particularly those working with large datasets.checked 12 Oct 2026
- Regression models
- Yeschecked 23 Sep 2026
- Sample weights
- The NumPy and JAX implementations support sample-weighted PLS modeling.checked 12 Oct 2026
- scikit-learn integration
- The scikit-learn-conformant PLS wrapper can be used with scikit-learn pipelines and cross-validation tools.checked 12 Oct 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Support
- The contribution guidelines direct users with questions or bug reports to search or open a GitHub issue.checked 12 Oct 2026
- Transformations
- NumPy and JAX implementations provide transform methods for dimensionality reduction to score space and inverse_transform methods for reconstruction.checked 12 Oct 2026
- Weighted modeling
- The package supports sample-weighted PLS modeling.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
