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NOTICE
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==============================================================
PGBM
Copyright 2021 Olivier Sprangers as part of Airlab Amsterdam
==============================================================
The core package makes use of:
PyTorch:
PyTorch (https://pytorch.org/)
Copyright (c) 2016- Facebook, Inc (Adam Paszke)
Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Numpy:
Numpy(https://github.com/numpy/numpy)
Copyright (c) 2005-2021, NumPy Developers.
Pandas:
Pandas(https://github.com/pandas-dev/pandas)
Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team. All rights reserved.
Copyright (c) 2011-2021, Open source contributors.
Numba:
Numba (http://numba.pydata.org/)
Copyright (c) 2012, Anaconda, Inc.
In the experimental section the following additional packages are used:
LightGBM (https://github.com/microsoft/LightGBM)
Copyright (c) Microsoft Corporation
Scikit-learn (https://github.com/scikit-learn/scikit-learn)
Copyright (c) 2007-2020 The scikit-learn developers.
Properscoring (https://github.com/TheClimateCorporation/properscoring)
Copyright (c) 2015 The Climate Corporation
NGBoost (https://github.com/stanfordmlgroup/ngboost)
Copyright (c) 2019 Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler
Matplotlib (https://matplotlib.org/)
Copyright (c) 2002 - 2012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team.
Copyright (c) 2012 - 2021 The Matplotlib development team.
We use the following datasets from the UCI Machine Learning Repository:
* [yacht](https://archive.ics.uci.edu/ml/datasets/yacht+hydrodynamics)
* [boston](https://archive.ics.uci.edu/ml/machine-learning-databases/housing/)
* [energy](https://archive.ics.uci.edu/ml/datasets/energy+efficiency)
* [concrete](https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/compressive/)
* [wine](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/)
* [power](https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant)
* [naval](http://archive.ics.uci.edu/ml/datasets/condition+based+maintenance+of+naval+propulsion+plants)
* [protein](https://archive.ics.uci.edu/ml/datasets/Physicochemical+Properties+of+Protein+Tertiary+Structure)
* [msd](https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD)
* [higgs](https://archive.ics.uci.edu/ml/datasets/HIGGS) (pre-download and extract to pgbm/datasets)
We use the following datasets from the openml archive:
* [kin8nm](https://www.openml.org/d/189)
We use the following datasets from Kaggle:
* [m5](https://www.kaggle.com/c/m5-forecasting-accuracy/data) (pre-download and extract to pgbm/datasets/m5)