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Add parallel computing support to antibiogram() and wisca() (#281) (#282)

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* Add parallel computing support to antibiogram() and wisca() (#281)

For WISCA: simulations are distributed across (group, chunk) job pairs
via future.apply::future_lapply(), keeping all workers active even when
the regimen count is smaller than nbrOfWorkers(). Sequential fallback
with progress ticker is preserved when parallel = FALSE or workers = 1.

For grouped antibiograms: each group is processed by a separate worker,
mirroring the row-batch approach in as.sir().

Same gate pattern as as.sir() (PR #280): requires a non-sequential
future::plan() to be active; auto-upgrades to parallel = TRUE when a
parallel plan is detected; throws an informative error otherwise.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix version to 3.0.1.9055 and update CLAUDE.md version formula

Uses origin/${defaultbranch} (with a fetch) instead of the local
branch ref so the commit count is never stale after a merge.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix non-ASCII characters in antibiogram.R

Replace en/em dashes and non-breaking spaces with ASCII equivalents
to satisfy R CMD check portability requirement.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Update auto-generated Rd files after documentation rebuild

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Move parallel gate to top of antibiogram.default() like sir.R

The gate was inside the wisca==TRUE block, so parallel=TRUE with a
sequential plan was silently ignored for non-WISCA antibiograms.
Now the gate runs unconditionally at the top of the function,
identical to the as.sir() pattern: error on explicit parallel=TRUE
with sequential plan, auto-upgrade when a non-sequential plan is
already active.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

* Fix parallel WISCA returning all NA; strengthen tests; add sequential hint

Bug: lapply() over a factor yields length-1 factor elements (integer
codes), while for() over a factor yields character strings.  The job
list stored j\$group as a factor integer, but the reassembly loop
compared it with identical(j\$group, g) where g was character -- always
FALSE, so no simulation chunks were ever assembled and coverage stayed
NA throughout.

Fix: convert unique_groups to character before building jobs so both
the job list and the reassembly loop use the same type.

Tests: replaced na.rm = TRUE guards with explicit anyNA() checks so the
test suite would have caught the all-NA result immediately.

Also adds a sequential-mode performance hint (analogous to sir.R
lines 1116-1127) when simulations >= 500 and >= 3 regimens.

https://claude.ai/code/session_01FC43syPbzhGmKgrrVNHjnF

---------

Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
2026-04-30 18:41:56 +01:00
committed by GitHub
parent 23beebc6c3
commit f7e9294bea
8 changed files with 279 additions and 79 deletions
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NEWS.md
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@@ -1,4 +1,4 @@
# AMR 3.0.1.9053
# AMR 3.0.1.9055
This will become release v3.1.0, intended for launch end of May.
@@ -7,6 +7,7 @@ This will become release v3.1.0, intended for launch end of May.
* Support for the [`future`](https://future.futureverse.org) package and its framework, as the previous implementation of parallel computing was slow
- **Breaking change**: `as.sir()` with `parallel = TRUE` now requires a non-sequential `future::plan()` to be active before the call — e.g., `future::plan(future::multisession)` — and throws an informative error if none is set.
- New all-core usage setup: when the number of AB columns is smaller than the number of available cores, rows are now split into batches so all cores stay active (row-batch mode). Previously, a 6-column dataset on a 16-core machine would only use 6 cores; now all 16 are used, with each worker processing a smaller row slice (lower per-worker memory pressure and processing time)
- `antibiogram()` and `wisca()` gained a `parallel` argument using the same `future`/`future.apply` pattern: for WISCA, Monte Carlo simulations are split into `(group, chunk)` job pairs distributed across workers; for grouped antibiograms, each group is processed by a separate worker (#281)
* Integration with the *tidymodels* framework to allow seamless use of SIR, MIC and disk data in modelling pipelines via `recipes`
- `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
- New `tidyselect` helpers: