https://github.com/lanl/t-elf
Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
https://github.com/lanl/t-elf
blind-source-separation dimensionality-reduction feature-extraction gpu high-performance-computing hpc latent-variables machine-learning matrix matrix-completion matrix-factorization non-negative-matrix-factorization pattern-extraction semi-supervised-learning tensor-decomposition tensor-factorization tensors text-preprocessing unsupervised-learning
Last synced: 3 months ago
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Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.
- Host: GitHub
- URL: https://github.com/lanl/t-elf
- Owner: lanl
- License: other
- Created: 2023-10-10T20:08:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-24T20:16:27.000Z (11 months ago)
- Last Synced: 2024-07-24T22:46:47.235Z (11 months ago)
- Topics: blind-source-separation, dimensionality-reduction, feature-extraction, gpu, high-performance-computing, hpc, latent-variables, machine-learning, matrix, matrix-completion, matrix-factorization, non-negative-matrix-factorization, pattern-extraction, semi-supervised-learning, tensor-decomposition, tensor-factorization, tensors, text-preprocessing, unsupervised-learning
- Language: Python
- Homepage: https://lanl.github.io/T-ELF/
- Size: 37.5 MB
- Stars: 6
- Watchers: 7
- Forks: 2
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Tensor Extraction of Latent Features (T-ELF) 
[](https://github.com/lanl/T-ELF/actions/workflows/ci_tests.yml/badge.svg?branch=main) [](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg) [](https://img.shields.io/badge/python-v3.11.10-blue) [](https://zenodo.org/doi/10.5281/zenodo.10257896)
### [:information_source: Documentation](https://lanl.github.io/T-ELF/) [:orange_book: Examples](examples/) [:page_with_curl: Publications](https://smart-tensors.lanl.gov/publications/) [:link: Website](https://smart-tensors.LANL.gov)
T-ELF is one of the machine learning software packages developed as part of the [R&D 100](https://smart-tensors.lanl.gov/news/rnd100_smarttensors/) winning **[SmartTensors AI](https://smart-tensors.lanl.gov/software/)** project at Los Alamos National Laboratory (LANL). T-ELF presents an array of customizable software solutions crafted for analysis of datasets. Acting as a comprehensive toolbox, T-ELF specializes in data pre-processing, extraction of latent features, and structuring results to facilitate informed decision-making. Leveraging high-performance computing and cutting-edge GPU architectures, our toolbox is optimized for analyzing large datasets from diverse set of problems.
Central to T-ELF's core capabilities lie non-negative matrix and tensor factorization solutions for discovering multi-faceted hidden details in data, featuring automated model determination facilitating the estimation of latent factors or rank. This pivotal functionality ensures precise data modeling and the extraction of concealed patterns. Additionally, our software suite incorporates cutting-edge modules for both pre-processing and post-processing of data, tailored for diverse tasks including text mining, Natural Language Processing, and robust tools for matrix and tensor analysis and construction.
T-ELF's adaptability spans across a multitude of disciplines, positioning it as a robust AI and data analytics solution. Its proven efficacy extends across various fields such as Large-scale Text Mining, High Performance Computing, Computer Security, Applied Mathematics, Dynamic Networks and Ranking, Biology, Material Science, Medicine, Chemistry, Data Compression, Climate Studies, Relational Databases, Data Privacy, Economy, and Agriculture.
## Installation
### Step 1: [Install Poetry to your system](https://python-poetry.org/docs/)
This step is optional. Use Pip or Conda if Poetry is not avaiable.
### Step 2: Install the Library
**Option 1: Install via Poetry or Pip**
```shell
conda create --name TELF python=3.11.10
source activate TELF # or
poetry install # or
```
**Option 2: Install via Conda**
```shell
git clone https://gitlab.lanl.gov/maksim/telf_internal
cd telf_internal
conda env create --file environment_gpu.yml # use for CPU only
conda activate TELF_conda
conda develop .
```
### Step 3: Post-installation Dependencies
Next, we need to install the optional and additional dependencies. These include optional dependencies for GPU and HPC capabilities, as well as required dependencies like the SpaCy language models.
To view all available options, please run:
```shell
python post_install.py --help
```
Install the additional dependencies:
```shell
python post_install.py # use the following, for example, for GPU system:
```
#### Jupyter Setup Tutorial for using the examples ([Link](https://www.maksimeren.com/post/conda-and-jupyter-setup-for-research/))
## Capabilities
### Please see our [:page_with_curl: Publications](https://smart-tensors.lanl.gov/publications/) for the capabilities
## Modules
### TELF.factorization
| **Method** | **Dense** | **Sparse** | **GPU** | **CPU** | **Multiprocessing** | **HPC** | **Description** | **Example** |
|:-------------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:-------------------:|:------------------:|:----------------------------------------------------------------:|:-----------:|
| NMFk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | NMF with Automatic Model Determination | [Link](examples/NMFk/NMFk.ipynb) |
| Custom NMFk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | Use Custom NMF Functions with NMFk | [Link](examples/NMFk/Custom_NMF_NMFk.ipynb) |
| TriNMFk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | NMF with Automatic Model Determination for Clusters and Patterns | [Link](examples/TriNMFk/TriNMFk.ipynb) |
| RESCALk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | RESCAL with Automatic Model Determination | [Link](examples/RESCALk/RESCALk.ipynb) |
| RNMFk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | Recommender NMFk | [Link](examples/RNMFk/RNMFk.ipynb) |
| SymNMFk | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | NMFk with Symmetric Clustering | [Link](examples/SymNMFk/SymNMFk.ipynb) |
| WNMFk | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | NMFk with weighting - used for recommendation system | [Link](examples/WNMFk/WNMFk.ipynb) |
| HNMFk | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | Hierarchical NMFk | [Link](examples/HNMFk/HNMFk.ipynb) |
| BNMFk | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | Boolean NMFk | [Link](examples/BNMFk/BNMFk.ipynb) |
| LMF | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | | | Logistic Matrix Factorization | [Link](examples/LMF/LMF.ipynb) |
| SPLIT | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | Joint NMFk factorization of multiple data via SPLIT | [Link](examples/SPLIT/00-SPLIT.ipynb) |
| SPLITTransfer | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | Supervised transfer learning method via SPLIT and NMFk | [Link](examples/SPLITTransfer/00-SPLITTransfer.ipynb) |
### TELF.pre_processing
| **Method** | **Multiprocessing** | **HPC** | **Description** | **Example** |
|:----------:|:-------------------:|:-------------------:|:------------------------------------------------------------------:|:-----------:|
| Vulture | :heavy_check_mark: | :heavy_check_mark: | Advanced text processing tool for cleaning and NLP | [Link](examples/Vulture) |
| Beaver | :heavy_check_mark: | :heavy_check_mark: | Fast matrix and tensor building tool for text mining | [Link](examples/Beaver) |
| iPenguin | :heavy_check_mark: | | Online information retrieval tool for Scopus, SemanticScholar, and OSTI | [Link](examples/iPenguin) |
| Orca | :heavy_check_mark: | | Duplicate author detector for text mining and information retrieval | [Link](examples/Orca) |
### TELF.post_processing
| **Method** | **Description** | **Example** |
|:----------:|:----------------------------------------------------------:|:-----------:|
| Wolf | Graph centrality and ranking tool | [Link](examples/Wolf) |
| Peacock | Data visualization and generation of actionable statistics | [Link](examples/Peacock) |
| SeaLion | Generic report generation tool | [Link](examples/SeaLion) |
| Fox | Report generation tool for text data from NMFk using OpenAI | [Link](examples/Fox) |
| ArcticFox | Report generation tool for text data from HNMFk using local LLMs | [Link](examples/ArcticFox) |
### TELF.applications
| **Method** | **Description** | **Example** |
|:----------:|:--------------------------------------------------------------------:|:-----------:|
| Cheetah | Fast search by keywords and phrases | [Link](examples/Cheetah) |
| Bunny | Dataset generation tool for documents and their citations/references | [Link](examples/Bunny) |
| Penguin | Text storage tool | [Link](examples/Penguin) |
| Termite | Knowladge graph building tool | :soon: |
## How to Cite T-ELF?
If you use T-ELF please cite.
**APA:**
```latex
Eren, M., Solovyev, N., Barron, R., Bhattarai, M., Truong, D., Boureima, I., Skau, E., Rasmussen, K., & Alexandrov, B. (2023). Tensor Extraction of Latent Features (T-ELF) [Computer software]. https://doi.org/10.5281/zenodo.10257897
```
**BibTeX:**
```latex
@software{TELF,
author = {Eren, Maksim and Solovyev, Nick and Barron, Ryan and Bhattarai, Manish and Truong, Duc and Boureima, Ismael and Skau, Erik and Rasmussen, Kim and Alexandrov, Boian},
month = oct,
title = {{Tensor Extraction of Latent Features (T-ELF)}},
url = {https://github.com/lanl/T-ELF},
doi = {10.5281/zenodo.10257897},
year = {2023}
}
```
## Authors
- [Maksim Ekin Eren](mailto:[email protected]): Information Systems and Modeling Group, Los Alamos National Laboratory ([Website](https://www.maksimeren.com/))
- [Nicholas Solovyev](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Ryan Barron](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Manish Bhattarai](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Duc Truong](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Ismael Boureima](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Erik Skau](mailto:[email protected]): Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory
- [Kim Rasmussen](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
- [Boian S. Alexandrov](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory
## Patents
>Boian ALEXANDROV, o. S. F., New Mexico, Maksim Ekin EREN, of Sante Fe, New Mexico, Manish BHATTARAI, of Albuquerque, New Mexico, Kim Orskov RASMUSSEN of Sante Fe, New Mexico, and Charles K. NICHOLAS, of Columbia, Maryland, (“Assignor”) DATA IDENTIFICATION AND CLASSIFICATION METHOD, APPARATUS, AND SYSTEM. No. 63/472,188. Triad National Security, LLC. (June 9, 2023).
>BS. Alexandrov, LB. Alexandrov, and VG. Stanev et al. 2020. Source identification by non-negative matrix factorization combined with semi-supervised clustering. US Patent S10,776,718 (2020).
## Copyright Notice
>© 2022. Triad National Security, LLC. All rights reserved.
This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
Department of Energy/National Nuclear Security Administration. All rights in the program are
reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear
Security Administration. The Government is granted for itself and others acting on its behalf a
nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare
derivative works, distribute copies to the public, perform publicly and display publicly, and to permit
others to do so.
**LANL C Number: C22048**
## License
This program is open source under the BSD-3 License.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and
the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
and the following disclaimer in the documentation and/or other materials provided with the
distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
## Developer Test Suite
Developer test suites are located under [```tests/```](tests/) directory. Tests can be ran from this folder using ```python -m pytest *```.
## LANL HPC Installation Notes
### Chicoma
```shell
# replace with your own path below.
conda create --prefix= python=3.11.10
source activate # or use conda activate <...>
pip install .
python post_install.py --gpu --hpc-conda
```
### Darwin
```shell
salloc -n 1 -p shared-gpu
module load openmpi
module load miniconda3
conda create --name TELF python=3.11.10
conda activate TELF # or
pip install .
python post_install.py --gpu --hpc
```