nirs4all.analysis.transfer_metrics module

Transfer Metrics Computation.

This module provides fast, optimized computation of transfer-focused metrics between two datasets in PCA space. Metrics are designed to assess how well preprocessing aligns datasets for transfer learning scenarios.

Metrics computed: - Centroid Distance: Euclidean distance between dataset centroids in PCA space - CKA (Centered Kernel Alignment): Representation similarity - Grassmann Distance: Angular distance between PCA subspaces - RV Coefficient: Multivariate correlation structure - Procrustes Disparity: Shape alignment after optimal transformation - Trustworthiness: Neighborhood preservation - Spread Distance: Distribution overlap combining covariance and sample distances