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توضیحات

Advancements on non-negative matrix factorization in PyTorch, with crystallography as a primary use case.

ویژگی	مقدار
سیستم عامل	-
نام فایل	constrained-matrix-factorization-0.1.2
نام	constrained-matrix-factorization
نسخه کتابخانه	0.1.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Phil Maffettone
ایمیل نویسنده	pmaffettone@bnl.gov
آدرس صفحه اصلی	https://github.com/NSLS-II/constrained-matrix-factorization
آدرس اینترنتی	https://pypi.org/project/constrained-matrix-factorization/
مجوز	BSD (3-clause)

================================ Constrained Matrix Factorization ================================ .. image:: https://img.shields.io/github/workflow/status/nsls-ii/constrained-matrix-factorization/Unit%20Tests :target: https://github.com/nsls-ii/constrained-matrix-factorization/actions?query=workflow%3A%22Unit+Tests%22+branch%3Amain .. image:: https://img.shields.io/pypi/v/constrained-matrix-factorization.svg :target: https://pypi.python.org/pypi/constrained-matrix-factorization Advancements on non-negative matrix factorization in PyTorch, with crystallography as a primary use case. * Free software: 3-clause BSD license * Documentation: https://nsls-ii.github.io/constrained-matrix-factorization. Features -------- * Torch enabled non-negative matrix factorization * Optional input components and/or weights * Rigidly constrained specific components or weights * Plotting utilities * Utilities for "elbow method" and automatic iterative cmf Wrappers for scientific use cases ================================= * :code:`scattering`: XRD, PDF, TS, SAXS Developer's Instructions ------------------------ Install from github:: $ python3 -m venv cmf_env $ source cmf_env/bin/activate $ git clone https://github.com/nsls-ii/constrained-matrix-factorization $ cd constrained-matrix-factorization $ python -m pip install --upgrade pip wheel $ python -m pip install -r requirements-dev.txt $ pre-commit install $ python -m pip install -e . Original Publication -------------------- This work was originally published on `the arXiv here of this paper <https://arxiv.org/abs/2104.00864>`_. It has since been peer reviewd and published in `Applied Physics Reviews <https://doi.org/10.1063/5.0052859>`_. Abstract ======== Non-negative Matrix Factorization (NMF) methods offer an appealing unsupervised learning method for real-time analysis of streaming spectral data in time-sensitive data collection, such as in situ characterization of materials. However, canonical NMF methods are optimized to reconstruct a full dataset as closely as possible, with no underlying requirement that the reconstruction produces components or weights representative of the true physical processes. In this work, we demonstrate how constraining NMF weights or components, provided as known or assumed priors, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch based method for efficiently applying constrained NMF and demonstrate this on several synthetic examples. When applied to streaming experimentally measured spectral data, an expert researcher-in-the-loop can provide and dynamically adjust the constraints. This set of interactive priors to the NMF model can, for example, contain known or identified independent components, as well as functional expectations about the mixing of components. We demonstrate this application on measured X-ray diffraction and pair distribution function data from in situ beamline experiments. Details of the method are described, and general guidance provided to employ constrained NMF in extraction of critical information and insights during in situ and high-throughput experiments.

نیازمندی

مقدار	نام
-	torch
-	numpy
-	scipy

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl constrained-matrix-factorization-0.1.2:

pip install constrained-matrix-factorization-0.1.2.whl

نصب پکیج tar.gz constrained-matrix-factorization-0.1.2:

pip install constrained-matrix-factorization-0.1.2.tar.gz