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

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Assessment

Difficulty
3/5
Estimated time
1-3 hours
Newbie friendliness
76/100
Issue type
Refactor
Clarity
Clearly specified
Activity status
Quiet
Tech stack
kotlin
Domain
machine-learning

Research direction

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.

Written by the indexing model from the issue text.

Description

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.

Dominant language
Kotlin
Stars
52
Forks
15
Avg merge
1d 15h
Merged PRs (30d)
36

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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