Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

[Lane 2 · kotlin-core] Precision/Recall/F1 — extract Accuracy's class-matching helpers into a shared internal file

Aperta
  • Qualcosa non va?
#1,225 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
3/5
Tempo stimato
1-3 ore
Idoneità per principianti
76/100
Tipo di issue
Refactoring
Chiarezza
Specificata chiaramente
Stato di attività
Tranquilla
Stack tecnologico
kotlin
Ambito
machine-learning

Direzione di ricerca

Start with Accuracy.kt and its existing tests, then move the listed class-matching helpers into ClassificationSupport.kt in the same package and route Accuracy.update through the shared per-sample entry point. Run ./gradlew :skainet-lang:skainet-lang-core:apiCheck and ./gradlew :skainet-backends:skainet-backend-cpu:jvmTest --tests "sk.ainet.exec.metrics.*". Done means the tests pass, the API dump is unchanged, and Accuracy.kt has no argmax or index-walking code of its own.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

coding good first issue refactor size:s skill:kotlin-core sub-issue training

Sub-issue of #1222 (Precision, Recall and F1Score metrics).

Lane: 2 · Kotlin core (preparatory refactor)
Skill needed: Kotlin. No tensor-internals knowledge needed — everything you move already exists and already has tests.
Size: s (~2–3 h)
Blocked by: nothing. Unblocks #1226.

What to do

Accuracy.kt (skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/nn/metrics/Accuracy.kt)
carries, as private members, all the class-matching machinery the three new metrics also
need: argmax, argmaxTarget, insertClassIndex, validateIndexTargetShapes,
validatePredictions, normalizeDim, iterateOverSamples, plus the "hard Int32 targets
vs. soft FP32/FP16 targets" dispatch and the binary-threshold branch in
computeWithIndexTargets / computeWithSoftTargets. Copying that three more times is the
duplication git blame will ask about in six months.

  1. Create ClassificationSupport.kt in the same package. Move the helpers there as
    internal top-level functions (or one internal object ClassificationSupport). Keep
    names and signatures; keep error messages, but make the "Accuracy …" prefixes
    metric-agnostic (pass the metric name in, or drop it).
  2. Add one internal entry point that does the per-sample walk once for both target
    kinds and hands (predictedClass, targetClass) pairs to a callback, e.g.
    internal inline fun forEachPrediction(predictions, targets, dim, threshold, action: (predicted: Int, target: Int) -> Unit).
    Accuracy.update becomes a ~5-line call to it that counts matches. This is what
    ConfusionMatrixAccumulator (#1226) will call too.
  3. Accuracy's public surface must not change: same constructor, same accuracy() /
    binaryAccuracy() factories, same behaviour. Run
    ./gradlew :skainet-lang:skainet-lang-core:apiCheck — the API dump must be unchanged
    (everything you add is internal).
  4. Run the existing tests unchanged:
    ./gradlew :skainet-backends:skainet-backend-cpu:jvmTest --tests "sk.ainet.exec.metrics.*".

Acceptance

  • Accuracy.kt contains no argmax / index-walking code of its own
  • apiCheck passes with no dump change
  • AccuracyTest passes unmodified
  • PR title refactor(metrics): extract classification helpers from Accuracy (#1222)

Notes

Pure refactor — no new behaviour, so no new tests needed beyond the existing ones passing.
If you find Accuracy has a latent bug while you're in there (for example the
threshold branch reads logit index 1 when classCount == 2 but index 0 when
classCount == 1 — check that this is intended), don't fix it here; open a separate
bug and link it on the parent.

Lingua principale
Kotlin
Stelle
52
Fork
15
Merge medio
1g 15h
PR unite (30g)
36

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di SKaiNET-developers/SKaiNET

Tutte le issue di SKaiNET-developers/SKaiNET

Issue simili

Altre issue su Kotlin

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.