sqfa 0.2.0

Supervised Quadratic Feature Analysis: Task specific second order feature extraction

SQFA

Supervised Quadratic Feature Analysis (SQFA) is a supervised dimensionality reduction technique. It learns a set of linear features that maximize the differences in first- and second-order statistics between classes, in a way that supports quadratic classifiers (e.g. QDA). The sqfa package implements SQFA.

For detailed information on the method and the package, see the sqfa package tutorials and the SQFA preprint, "Supervised Quadratic Feature Analysis: An Information Geometry Approach to Dimensionality Reduction".

Overview

The package provides the class SQFA that can be used to train the model. The class has an API similar to sklearn. An example of how to use the SQFA class is shown below:

import sqfa
import torchvision

### DOWNLOAD DATASET

trainset = torchvision.datasets.MNIST(
    root="./data", train=True, download=True
)
X = trainset.data.reshape(-1, 28 * 28).float()
y = trainset.targets
# Scale X
X = X / (X.std() * 28.0)
X = X - X.mean(dim=0, keepdim=True)

### INITIALIZE SQFA MODEL

model = sqfa.model.SQFA(
    n_dim=X.shape[-1],
    n_filters=4,
    feature_noise=0.01,
)

### FIT MODEL. TWO OPTIONS:

# 1) Give data and labels as input
model.fit_pca(X) # Optional: PCA initialization to speed up convergence
model.fit(X=X, y=y)

# 2) Give dictionary with data statistics as input
data_stats = sqfa.statistics.class_statistics(X, y)
model.fit_pca(data_statistics=data_stats) # Optional: PCA initialization to speed up convergence
model.fit(data_statistics=data_stats)

### TRANSFORM DATA AND SECOND MOMENTS TO THE LEARNED FEATURE SPACE

X_transformed = model.transform(X).detach()
second_moments_transformed = model.transform_scatters(
    data_stats["second_moments"],
).detach()

Installation

Virtual environment

We recommend installing the package in a virtual environment. For this, you can first install miniconda (install instructions link), and then create a virtual environment with Python 3.11 using the following shell command:

conda create -n my-sqfa python=3.11

You can then activate the virtual environment with the following command:

conda activate my-sqfa

You should activate the my-sqfa environment to install the package, and every time you want to use it.

Install package from PyPI

The easiest way to install the package is form the PyPI repository. To install the package and the dependencies needed to run the tutorials, use the following command:

pip install sqfa[dev]

To install the lighter version without the tutorials dependencies, use

pip install sqfa

Install package from source

To install the package from source (e.g., if you want to modify the code), you can clone the repository and install the package in editable mode with the following commands:

git clone https://github.com/dherrera1911/sqfa.git
cd sqfa
pip install -e .[dev]

Citation

Please cite the SQFA preprint if you use the package:

@misc{herreraesposito2025supervisedquadraticfeatureanalysis,
      title={Supervised Quadratic Feature Analysis: An Information Geometry Approach to Dimensionality Reduction}, 
      author={Daniel Herrera-Esposito and Johannes Burge},
      year={2025},
      eprint={2502.00168},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/2502.00168}, 
}