Science Score: 77.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
3 of 10 committers (30.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary
Repository
Management of Benchmark Instances and Instance Attributes
Basic Info
- Host: GitHub
- Owner: Udopia
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://udopia.github.io/gbd/
- Size: 45.9 MB
Statistics
- Stars: 20
- Watchers: 2
- Forks: 7
- Open Issues: 1
- Releases: 14
Metadata Files
README.md
Global Benchmark Database (GBD)
GBD is a comprehensive suite of tools for provisioning and sustainably maintaining benchmark instances and their metadata for empirical research on hard algorithmic problem classes. For an introduction to the GBD concept, the underlying data model, and specific use cases, please refer to our 2024 SAT Tool Paper.
GBD contributes data to your algorithmic evaluations
GBD provides benchmark instance identifiers, feature extractors, and instance transformers for hard algorithmic problem domains, now including propositional satisfiability (SAT) and optimization (MaxSAT), and pseudo-Boolean optimization (PBO).
GBD solves several problems
- benchmark instance identification
- identification of equivalence classes of benchmark instances
- distribution of benchmark instances and benchmark metadata
- initialization and maintenance of instance feature databases
- transformation algorithms for benchmark instances
GBD provides an extensible set of problem domains, feature extractors, and instance transformers. For a description of those currently supported, see the GBDC documentation. GBDC is a Python extension module for GBD's performance-critical code (written in C++), maintained in a separate repository.
Installation and Configuration
- Run
pip install gbd-tools - Run
pip install gbdc(optional, installation of extension module gbdc) - Obtain a GBD database, e.g. download https://benchmark-database.de/getdatabase/meta.db.
- Configure your environment by registering paths to databases like this
export GBD_DB=path/to/database1:path/to/database2. - Test the command line interface with the
gbd infoandgbd --helpcommands.
GBD Interfaces
GBD provides the command-line tool gbd, the web interface gbd serve, and the Python interface gbd_core.api.GBD.
GBD Command-Line Interface
Central commands in gbd are those for data access gbd get and database initialization gbd init.
See gbd --help for more commands.
Once a database is registered in the environment variable GBD_DB, the gbd get command can be used to access data.
See gbd get --help for more information.
gbd init provides access to registered feature extractors, such as those provided by the gdbc extension module.
All initialization routines can be run in parallel, and resource limits can be set per process.
See gbd init --help for more information.
GBD Server
The GBD server can be started locally with gbd serve. Our instance of the GBD server is hosted at https://benchmark-database.de/. You can download benchmark instances and prebuilt feature databases from there.
GBD Python Interface
The GBD Python interface is used by all programs in the GBD ecosystem. Important here is the query command, which returns GBD data in the form of a Pandas dataframe for further analysis, as shown in the following example.
Python
from gbd_core.api import GBD
with GBD(['path/to/database1', 'path/to/database2', ..] as gbd:
df = gbd.query("family = hardware-bmc", resolve=['verified-result', 'runtime-kissat'])
Scripts and use cases of GBD's Python interface are available on https://udopia.github.io/gbdeval/. The evaluation demo demonstrates portfolio analysis and subsequent category-wise performance evaluation using the 2023 SAT competition data. The prediction demo demonstrates category prediction from instance features and subsequent feature importance evaluation.
Owner
- Name: Markus Iser
- Login: Udopia
- Kind: user
- Location: Karlsruhe
- Company: Karlsruhe Institute of Technology
- Website: https://algo2.iti.kit.edu/3986.php
- Twitter: udopia
- Repositories: 12
- Profile: https://github.com/Udopia
Automated Reasoning and Optimization, Meta Algorithmics, Explainable Artificial Intelligence, Algorithm Visualization and Sonification
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Iser" given-names: "Markus" orcid: "https://orcid.org/0000-0003-2904-232X" - family-names: "Jabs" given-names: "Christoph" orcid: "https://orcid.org/0000-0003-3532-696X" title: "GBD Tools" version: 4.7.0 doi: 10.5281/zenodo.10213944 date-released: 2023-11-28 url: "https://github.com/Udopia/gbd"
GitHub Events
Total
- Watch event: 2
- Push event: 6
- Pull request review event: 1
- Pull request event: 5
- Fork event: 1
Last Year
- Watch event: 2
- Push event: 6
- Pull request review event: 1
- Pull request event: 5
- Fork event: 1
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 1,039
- Total Committers: 10
- Avg Commits per committer: 103.9
- Development Distribution Score (DDS): 0.566
Top Committers
| Name | Commits | |
|---|---|---|
| Markus Iser | m****r@k****u | 451 |
| Luca | l****r@y****m | 285 |
| Luca Springer | u****v@s****u | 207 |
| Markus Iser | 2****r@g****m | 47 |
| Luca Springer | 3****r@u****m | 23 |
| Martin Heil | m****n@s****e | 15 |
| Elizaveta Danilova | u****v@s****u | 8 |
| Administrator | e****m@s****e | 1 |
| i12admin | i****n@i****e | 1 |
| The Codacy Badger | b****r@c****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 9 months ago
All Time
- Total issues: 7
- Total pull requests: 27
- Average time to close issues: 17 days
- Average time to close pull requests: 6 days
- Total issue authors: 3
- Total pull request authors: 5
- Average comments per issue: 1.43
- Average comments per pull request: 0.11
- Merged pull requests: 22
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 7
- Average time to close issues: 7 days
- Average time to close pull requests: 3 days
- Issue authors: 1
- Pull request authors: 3
- Average comments per issue: 2.0
- Average comments per pull request: 0.0
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Jakob-Bach (4)
- SimuIacron (2)
- chrjabs (1)
Pull Request Authors
- Weitspringer (14)
- fata-cmd (6)
- chrjabs (6)
- FreGeh (4)
- Udopia (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- pypi 1,011 last-month
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 4
(may contain duplicates) - Total versions: 284
- Total maintainers: 2
pypi.org: gbd-tools
GBD Tools: Maintenance and Distribution of Benchmark Instances and their Attributes
- Homepage: https://github.com/Udopia/gbd
- Documentation: https://gbd-tools.readthedocs.io/
- License: mit
-
Latest release: 4.9.11
published 10 months ago
Rankings
Maintainers (1)
pypi.org: global-benchmark-database-tool
Superseded by: gbd-tools
- Homepage: https://github.com/Udopia/gbd
- Documentation: https://global-benchmark-database-tool.readthedocs.io/
- License: GNU General Public License v3 (GPLv3)
-
Latest release: 2.9.0
published over 5 years ago
Rankings
Maintainers (2)
Dependencies
- flask *
- flask_limiter *
- iteration_utilities *
- matplotlib *
- mip *
- numpy *
- pandas *
- pebble *
- piskle *
- setuptools *
- sklearn *
- tatsu *
- waitress *
- wheel *
- actions/checkout v1 composite
- actions/checkout master composite
- actions/setup-python v2 composite
- peaceiris/actions-gh-pages v3 composite
- udopia/gbd latest
- udopia/nginx latest
- flask *
- pandas *
- pebble *
- tatsu *
- waitress *