cvmatrix 1.1.0

Fast computation of possibly centered/scaled training set kernel matrices in a cross-validation setting.

CVMatrix

The cvmatrix package implements the fast cross-validation algorithms by Engstrøm [1] for computation of training set $\mathbf{X}^{\mathbf{T}}\mathbf{X}$ and $\mathbf{X}^{\mathbf{T}}\mathbf{Y}$ in a cross-validation setting. In addition to correctly handling arbitrary row-wise pre-processing, the algorithms allow for and efficiently and correctly handle any combination of column-wise centering and scaling of X and Y based on training set statistical moments.

For an implementation of the fast cross-validation algorithms combined with Improved Kernel Partial Least Squares [2], see the Python package ikpls.

Installation

• Install the package for Python3 using the following command:

  pip3 install cvmatrix
  

• Now you can import the class implementing all the algorithms with:

  from cvmatrix.cvmatrix import CVMatrix
  

Quick Start

Use the cvmatrix package for fast computation of training set kernel matrices

import numpy as np
from cvmatrix.cvmatrix import CVMatrix

N = 100  # Number of samples.
K = 50  # Number of features.
M = 10  # Number of targets.

X = np.random.uniform(size=(N, K)) # Random X data
Y = np.random.uniform(size=(N, M)) # Random Y data
folds = np.arange(100) % 5 # 5-fold cross-validation

# Instantiate CVMatrix
cvm = CVMatrix(
    folds=folds,
    center_X=True,
    center_Y=True,
    scale_X=True,
    scale_Y=True,
)
# Fit on X and Y
cvm.fit(X=X, Y=Y)
# Compute training set XTX and/or XTY for each fold
for fold in cvm.folds_dict.keys():
    # Get both XTX and XTY
    training_XTX, training_XTY = cvm.training_XTX_XTY(fold)
    # Get only XTX
    training_XTX = cvm.training_XTX(fold)
    # Get only XTY
    training_XTY = cvm.training_XTY(fold)

Examples

In examples, you will find:

• Compute training matrices with CVMatrix

Benchmarks

In benchmarks, we have benchmarked cross-validation of the fast algorithms in cvmatrix against the baseline algorithms implemented in NaiveCVMatrix.

Left: Benchmarking cross-validation with the CVMatrix implementation versus the baseline implementation using three common combinations of (column-wise) centering and scaling. Right: Benchmarking cross-validation with the CVMatrix implementation for all possible combinations of (column-wise) centering and scaling. Here, most of the graphs lie on top of eachother. In general, no preprocessing is faster than centering which, in turn, is faster than scaling.

Contribute

To contribute, please read the Contribution Guidelines.

References

1. Engstrøm, O.-C. G. (2024). Shortcutting Cross-Validation: Efficiently Deriving Column-Wise Centered and Scaled Training Set $\mathbf{X}^\mathbf{T}\mathbf{X}$ and $\mathbf{X}^\mathbf{T}\mathbf{Y}$ Without Full Recomputation of Matrix Products or Statistical Moments
2. Dayal, B. S., & MacGregor, J. F. (1997). Improved PLS algorithms. Journal of Chemometrics, 11(1), 73-85.