Metadata-Version: 2.1
Name: SupervisedMF
Version: 0.0.4
Summary: A package for various supervised matrix factorization methods
Author-email: Agam Goyal <agamg2@illinois.edu>, Yi Wei <ywei224@wisc.edu>, Hanbaek Lyu <hlyu@math.wisc.edu>
Maintainer-email: Agam Goyal <agamg2@illinois.edu>
Project-URL: Homepage, https://github.com/ljw9510/SMF/tree/main
Project-URL: Issues, https://github.com/ljw9510/SMF/issues
Keywords: supervised matrix factorization,matrix factorization,dimensionality reduction,low-rank compression,classification
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=2.0.0
Requires-Dist: scipy>=1.13.0
Requires-Dist: scikit-learn>=1.5.1
Requires-Dist: matplotlib>=3.9.0
Requires-Dist: pandas>=2.2.2
Requires-Dist: seaborn>=0.13.0
Requires-Dist: tqdm>=4.66.4

# Supervised Matrix Factorization

This Python package contains source codes for algorithms for Supervised Matrix Factorization (SMF) in the papers [1] and [2]: 

## Installation

To install the package, run the following command in your environment:

```
python3 -m pip install SupervisedMF
```

Check your installation by trying to import the main classes in this package:

```
>>> from SMF import SMF_BCD
>>> from SMF import SMF_LPGD
```

## Pytorch Version

If you are looking to use the Pytorch version of the Supervised Matrix Factorization algorithms, please first install `torch` and its related dependencies in your environment using the appropriate command from [the official installation page](https://pytorch.org/get-started/locally/). 

For example, if you want to install `torch` for Linux with CUDA 12.1 using `pip`, run the following command:

```
pip3 install torch torchvision torchaudio
```

## References

[1] Lee, Joowon, Hanbaek Lyu, and Weixin Yao. [*"Exponentially convergent algorithms for supervised matrix factorization."*](https://papers.nips.cc/paper_files/paper/2023/hash/f2c80b3c9cf8102d38c4b21af25d9740-Abstract-Conference.html) Advances in Neural Information Processing Systems 36 (2024).

[2] Lee, Joowon, Hanbaek Lyu, and Weixin Yao. [*"Supervised Matrix Factorization: Local Landscape Analysis and Applications."*](https://proceedings.mlr.press/v235/lee24p.html) Forty-first International Conference on Machine Learning (2024).
