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Author:
Mario Koddenbrock
License:
MIT License
Copyright (c) 2026 KI-Werkstatt
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated docum...
Summary:
A large-scale benchmark for machine learning on Raman spectroscopy data
Latest version:
1.4.0
Required dependencies:
matplotlib
|
numpy
|
pandas
|
pyarrow
|
python-dotenv
|
raman-data
|
scikit-learn
|
seaborn
|
torch
|
tqdm
Optional dependencies:
adjusttext
|
aeon
|
aplr
|
autogluon-common
|
autogluon-core
|
autogluon-features
|
autogluon-tabular
|
autorank
|
bencheval
|
black
|
category-encoders
|
causilo
|
ctboost
|
einops
|
fastai
|
iltm
|
imodels
|
ipykernel
|
isort
|
jupyter
|
kditransform
|
myst-parser
|
nbsphinx
|
numba
|
plotly
|
pre-commit
|
pynvml
|
pytest
|
pytest-cov
|
pywavelets
|
raman-bench
|
ramanspy
|
ruff
|
setuptools
|
sktime
|
sphinx
|
sphinx-autodoc-typehints
|
sphinx-rtd-theme
|
tabpfn
|
tabpfn-extensions
|
tabpfnwide
|
tabstar
|
torchvision
|
xrfm
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