jmschrei / apricot
apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html
Why this job: works in the areas we cover, but does not say which job it does
What it does
Submodular optimization is a well studied field that focuses on set functions which have the *diminishing return* property. These set functions have the dimishing return property, such that adding an item to a set produces a smaller marginal gain than adding the same item to a superset of that set. More formally, for , the property is . When these functions represent a notion of diversity, finding the subset of examples that maximize these functions corresponds to finding a minimally redundant set.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 12 months ago
- Stars
- 538
- +1 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- 0.6.1
- Released 2 years ago
- Built with
- Jupyter Notebook
- 88% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 64% closed
- 12 still open
- Pull requests
- 15 merged
- 5 waiting
- Releases
- 2
- 0 in the last year
- No contributing guide
- No security policy
- No code of conduct
More: languages, topics and project record
Languages
- Jupyter Notebook 88%
- Python 12%
Topics
- python
- submodular-optimization
- submodularity
- data-science
- machine-learning
Project record
- JobOther AI tools
- StatusListed
- Commits189
- Forks53
- Watching6
- Stars in 28 days+1
- CreatedAug 12, 2018
- First seen by usNov 7, 2026
- CheckedNov 8, 2026