VIPurPCA

VIPurPCA offers a visualization of uncertainty propagated through the dimensionality reduction technique Principal Component Analysis (PCA) by automatic differentiation.

Installation

VIPurPCA requires Python 3.7.3 or later and can be installed via:

pip install vipurpca

A website showing results and animations can be found here.

Usage

Propagating uncertainty through PCA and visualize output uncertainty as animated scatter plot

In order to propagate uncertainty through PCA the class PCA can be used, which has the following parameters, attributes, and methods:

Example datasets

Two example datasets can be loaded after installing VIPurPCA providing mean, covariance and labels.

from vipurpca import load_data
Y, cov_Y, y = load_data.load_studentgrades_dataset()
Y, cov_Y, y = load_data.load_estrogen_dataset()

More information on the datasets can be found here

Example

from vipurpca import load_data
from vipurpca import PCA

# load mean (Y), uncertainty estimates (cov_Y) and labels (y)
Y, cov_Y, y = load_data.load_estrogen_dataset()
pca = PCA(matrix=Y, sample_cov=None, feature_cov=None,
full_cov=cov_Y, n_components=3, axis=0)
# compute PCA
pca.pca_value()
# Bayesian inference
pca.compute_cov_eigenvectors(save_jacobian=False)# Create animation
pca.animate(1, 2, labels=y, outfile='animation.gif')

The resulting animation can be found here here.