Metadata-Version: 2.1
Name: nemo-bo
Version: 0.1.3
Summary: Multi-objective optimization of chemical processes with automated machine learning model selection
Home-page: https://github.com/sustainable-processes/NEMO
License: MIT
Keywords: machine-learning,bayesian-optimization,multi-objective-optimization
Author: Simon Sung
Author-email: simon.sung06@gmail.com
Requires-Python: >=3.10,<4.0
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: botorch (>=0.6.4,<0.7.0)
Requires-Dist: forestci (>=0.5.1,<0.6.0)
Requires-Dist: gpytorch (>=1.6.0,<2.0.0)
Requires-Dist: hyperopt (>=0.2.7,<0.3.0)
Requires-Dist: matplotlib (>=3.5.2,<4.0.0)
Requires-Dist: ngboost (>=0.3.12,<0.4.0)
Requires-Dist: numba (>=0.55.1,<0.56.0)
Requires-Dist: numpy (>=1.21.6,<2.0.0)
Requires-Dist: openpyxl (>=3.0.9,<4.0.0)
Requires-Dist: pandas (>=1.4.2,<2.0.0)
Requires-Dist: pymoo (>=0.5.0,<0.6.0)
Requires-Dist: scikit-learn (>=1.1.1,<2.0.0)
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Requires-Dist: tensorflow-probability (>=0.17.0,<0.18.0)
Requires-Dist: torch (>=1.11.0,<2.0.0)
Requires-Dist: xgboost-distribution (>=0.2.4,<0.3.0)
Project-URL: Repository, https://github.com/sustainable-processes/NEMO
Description-Content-Type: text/markdown

#nemo

Multi-objective optimization of chemical processes with automated machine learning model selection
