contrastive_inverse_regression

cir is a python package provided the algorithm for contrastive inverse regression (CIR) for dimension reduction used in a supervised setting to find a low-dimensional representation by solving a nonconvex optimization problem on the Stiefel manifold.

Installation

Make sure you have numpy, pandas, and scipy install beforehand and the version of these packages compatible with cir. The easy way to install is using pip:

pip install contrastive-inverse-regression

Alternatively, you can also install by cloning this repository:

pip install git+https://github.com/myueen/cir.git

Dependencies

• Python (>= 3.10.9)
• numpy (>= 1.24.3)
• pandas (>= 2.1.4)
• scipy (>= 1.9.3)

To run exmaple, matplotlib (>= 3.8.2) is required

Example

The dataset for the following example is included in the example/dataset folder.

import contrastive_inverse_regression
from contrastive_inverse_regression import CIR
import pandas as pd

d = 2
alpha = 0.0001

# download the dataset and set it to the absolute path in your computer 
fg = pd.read_csv('../foregroundX.csv')
bg = pd.read_csv('../backgroundX.csv')
Y = pd.read_csv('../foregroundY.csv')
Yt = pd.read_csv('../backgroundY.csv')

fg = fg.iloc[0:, 1:]
bg = bg.iloc[0:, 1:]
Y = Y.iloc[0:, 1:]
Yt = Yt.iloc[0:, 1:]

V = CIR(fg, Y, bg, Yt, alpha, d)

Other detailed examples for employing cir are provided.

For the case of discrete foreground Y values, the mouse protein dataset Data_Cortex_Nuclear.csv is used and the corresponding visualization in mp_regression.py and regression testing in mp_regression.py.

For the case of continuous foreground Y values, cir is applied on the retinol dataset Retinol.txt and the corresponding regression is in plasma_regression.py. Continuous values are not usually for classification, hence visualization is not provided.

Citing contrastive-inverse-regression

If you find this algorithm helpful in your research, please add the following bibtex citation in references.

@phdthesis{hawke2023contrastive,
  title={Contrastive inverse regression for dimension reduction},
  author={Hawke, Sam and Luo, Hengrui and Li, Didong},
  journal={arXiv preprint arXiv:2305.12287},
  year={2023}
}

References

.. [1] : Hawke, S., Luo, H., & Li, D. (2023) "Contrastive Inverse Regression for Dimension Reduction", Retrieved from https://arxiv.org/abs/2305.12287

.. [2] Harry Oviedo (2024). SGPM for minimization over the Stiefel Manifold (https://www.mathworks.com/matlabcentral/fileexchange/73505-sgpm-for-minimization-over-the-stiefel-manifold), MATLAB Central File Exchange. Retrieved January 12, 2024.