UMAP-Based Local Biplot

Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability.

Please, if you use this code, cite this paper: Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

Requeriments

localbiplot requires Python >= 3.8 and internet access to download the libraries

Install from source code

!pip install -U git+https://github.com/Jectrianama/python-gcpds.localbiplot.git --quiet

Add the library in your code as follows:

import gcpds.localbiplot as lb

Documentation Overview

• User Manual
  • UMAP-Based Local Biplot
• Installing Our Local Biplot Library
  • Import Necessary Libraries
• Uniform Manifold Approximation and Projection (UMAP) Fundamentals
  • Swiss Roll Example
  • Initializing a UMAP Model.
  • Scatter Plot of the UMAP-reduced Data.
• Local Biplot Examples
  • Multivariate Gaussians
  • Forage Grasses Dataset

Code Reference

• Index

• Module Index

• Search Page