Machine learning pipeline to learn and predict assignment patterns in shift plans. Trains LightGBM on historical rosters, calibrates with isotonic regression, and for each shift outputs a probability for each employee to be assigned, plus ranked recommendations, optional fairness caps, and a visualization/reporting CLI.
active 2026-01-20 → 2026-02-13 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 25 days from 2026-01-20 to 2026-02-13. Pushes: 3 total, peak 2 in a day. Pull requests: 2 total, peak 2 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| chrisschaffner | 5 | 3 | 2 | 0 |
Recent activity
Latest issues, pull requests and releases
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.