mt-smac
A novel approach designed for optimizing highly parametrized algorithms.
Science Score: 44.0%
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Repository
A novel approach designed for optimizing highly parametrized algorithms.
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Metadata Files
README.md
MT-SMAC: A Novel Multi-Target Approach to Highly Parametrized Algorithms Optimization
💡 Introduction
MT-SMAC (Multi-Target Sequential Model-based Algorithm Configuration) is a new approach designed for optimizing highly parametrized algorithms built on top of SMAC. It offers a unique approach to tackling complex optimization problems. MT-SMAC stands out for its ability to handle multiple targets simultaneously, paving the way for more robust and versatile algorithmic solutions.
🧑🏫️ How it works
From the Paper:
This paper investigates the optimization of algorithms with many parameters, a common challenge in areas such as SAT solving, mixed integer programming, AI planning, and machine learning, through Automated Algorithm Configuration (AAC). We compare model-free and model-based AAC methods, with a focus on Sequential Model-Based Optimization (SMBO) and its application in Sequential Model-based Algorithm Configuration (SMAC). We explore multi-objective optimization, analyzing the performance of ParEGO and MO-SMAC. The core contribution of this work is the development of MT-SMAC, a multi-target model using a single surrogate model for all objectives, leveraging a Multi-task Gradient Boosting Machine to achieve a more efficient prediction process by understanding correlations between targets. The paper's empirical section utilizes the YAHPO gym for benchmarking, providing a comparative analysis of the proposed models. Concluding remarks suggest future research directions, including the exploration of Predicted Hypervolume Improvement and the potential of cross-validation to prevent overfitting, aiming to refine the process of algorithm configuration further.
🛠️ Installation
Python version: 3.10
```bash
Create and activate environment:
conda create -n SMAC python=3.10 conda activate SMAC
Install swig:
conda install gxxlinux-64 gcclinux-64 swig
Install SMAC via PyPI:
pip install smac ```
🚀 Quick Start
Run notebook evaltemplate-yahpo.ipynb
Owner
- Name: Marco Di Francesco
- Login: MarcoDiFrancesco
- Kind: user
- Location: Sweden
- Website: marcodifrancesco.com
- Repositories: 28
- Profile: https://github.com/MarcoDiFrancesco
Data Science • ABB
Citation (CITATION.cff)
---
cff-version: 1.2.0
message: "If you used SMAC in one of your research projects, please cite us:"
title: "SMAC3"
date-released: "2016-08-17"
url: "https://automl.github.io/SMAC3/master/index.html"
repository-code: "https://github.com/automl/SMAC3"
version: "1.0.1"
type: "software"
keywords:
- "blackbox optimization"
- "optimization"
- "bayesian optimization"
- "algorithm configuration"
- "machine learning"
- "algorithms"
license: "BSD-3-Clause"
authors:
- family-names: "Lindauer"
given-names: "Marius"
affiliation: "Leibniz Universität Hannover"
- family-names: "Eggensperger"
given-names: "Katharina"
orcid: "https://orcid.org/0000-0002-0309-401X"
affiliation: "University of Freiburg, Germany"
- family-names: "Feurer"
given-names: "Matthias"
orcid: "https://orcid.org/0000-0001-9611-8588"
affiliation: "University of Freiburg, Germany"
- family-names: "Biedenkapp"
given-names: "André"
orcid: "https://orcid.org/0000-0002-8703-8559"
affiliation: "University of Freiburg, Germany"
- family-names: "Deng"
given-names: "Difan"
affiliation: "Leibniz Universität Hannover"
- family-names: "Benjamins"
given-names: "Carolin"
affiliation: "Leibniz Universität Hannover"
- family-names: "Sass"
given-names: "René"
affiliation: "Leibniz Universität Hannover"
- family-names: "Hutter"
given-names: "Frank"
affiliation: "University of Freiburg, Germany"
- family-names: "Falkner"
given-names: "Stefan"
orcid: "https://orcid.org/0000-0002-6303-9418"
affiliation: "Bosch Center for Artificial Intelligence, Rennigen, Germany"
preferred-citation:
type: "article"
title: "SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization"
month: "9"
year: "2021"
url: "https://arxiv.org/abs/2109.09831"
authors:
- family-names: "Lindauer"
given-names: "Marius"
affiliation: "Leibniz Universität Hannover"
- family-names: "Eggensperger"
given-names: "Katharina"
orcid: "https://orcid.org/0000-0002-0309-401X"
affiliation: "University of Freiburg, Germany"
- family-names: "Feurer"
given-names: "Matthias"
orcid: "https://orcid.org/0000-0001-9611-8588"
affiliation: "University of Freiburg, Germany"
- family-names: "Biedenkapp"
given-names: "André "
orcid: "https://orcid.org/0000-0002-8703-8559"
affiliation: "University of Freiburg, Germany"
- family-names: "Deng"
given-names: "Difan"
affiliation: "Leibniz Universität Hannover"
- family-names: "Benjamins"
given-names: "Carolin"
affiliation: "Leibniz Universität Hannover"
- family-names: "Sass"
given-names: "René"
affiliation: "Leibniz Universität Hannover"
- family-names: "Hutter"
given-names: "Frank"
affiliation: "University of Freiburg, Germany"
...
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Dependencies
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- peter-evans/create-pull-request v3 composite
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- conda-incubator/setup-miniconda v2 composite
- dawidd6/action-send-mail v3 composite
- lee-dohm/select-matching-issues v1 composite
- ConfigSpace >=0.6
- matplotlib *
- pandas *
- seaborn *
- ConfigSpace >=0.6.1
- dask *
- joblib *
- numpy >=1.23.3
- psutil *
- pynisher >=1.0.0
- pyrfr >=0.9.0
- scikit-learn >=1.1.2
- scipy >=1.9.2