On the recordRead 12 Oct 202622 facts
IKPLS
On the record
- Algorithms
- It implements Improved Kernel PLS Algorithms 1 and 2 for CPU, GPU, and TPU use.ikpls.readthedocs.io · read 12 Oct 2026
- Audience
- The project describes its intended users as researchers and practitioners doing PLS modeling.ikpls.readthedocs.io · read 12 Oct 2026
- Compute
- Its NumPy implementations run on CPUs, and its JAX implementations support CPUs, GPUs, and TPUs.ikpls.readthedocs.io · read 12 Oct 2026
- Deployment
- desktopikpls.readthedocs.io · read 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.ikpls.readthedocs.io · read 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.ikpls.readthedocs.io · read 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.ikpls.readthedocs.io · read 12 Oct 2026
- Installation
- The package installs with pip using `pip3 install ikpls`; JAX implementations require the optional `jax` extra.ikpls.readthedocs.io · read 12 Oct 2026
- Installation requirement
- PyPI lists the requirement as Python >=3.10 and <3.15.pypi.org · read 12 Oct 2026
- JAX
- Optional JAX implementations run on CPU, GPU, and TPU and support end-to-end differentiation.ikpls.readthedocs.io · read 12 Oct 2026
- License
- PyPI lists the package under the Apache-2.0 license.pypi.org · read 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.ikpls.readthedocs.io · read 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.ikpls.readthedocs.io · read 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.ikpls.readthedocs.io · read 12 Oct 2026
- Regression models
- Yesikpls.readthedocs.io · read 23 Sep 2026
- Sample weights
- The NumPy and JAX implementations support sample-weighted PLS modeling.ikpls.readthedocs.io · read 12 Oct 2026
- scikit-learn integration
- The scikit-learn-conformant PLS wrapper can be used with scikit-learn pipelines and cross-validation tools.ikpls.readthedocs.io · read 12 Oct 2026
- Scripting support
- Yesikpls.readthedocs.io · read 23 Sep 2026
