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[Lane 2 · kotlin-core] F1Score metric

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Evaluación

Dificultad
3/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
57/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Tranquilo
Stack tecnológico
kotlin
Área
machine-learning

Línea de trabajo

Read Precision.kt and Recall.kt from the blocking issues, then inspect the accumulator and Metric.kt before implementing F1Score.kt at the specified core path. Add the specified tests in F1ScoreTest.kt and run the JVM metrics tests plus the API dump command. Done means the listed cases pass and the public API appears in the dump; this issue is blocked by #1227 and #1228 (or at least #1226).

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

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

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

Lane: 2 · Kotlin core
Skill needed: Kotlin. Read Precision.kt / Recall.kt from #1227 / #1228 first — F1Score composes their formulas over one accumulator.
Size: s (~2–3 h)
Blocked by: #1227 and #1228 (or at least #1226 — F1 only needs the accumulator, but its tests cross-check against Precision/Recall).

What to do

  1. Add public class F1Score(averaging: Averaging = Averaging.MACRO, dim: Int = -1, threshold: Float? = null) : Metric
    in skainet-lang/skainet-lang-core/src/commonMain/kotlin/sk/ainet/lang/nn/metrics/F1Score.kt:
    • override val name = "f1";
    • one private ConfusionMatrixAccumulator — do not hold a Precision and a
      Recall and call update() on both; that walks every batch twice;
    • compute() = reduce(acc.counts(), averaging) { tp, fp, fn -> per-class F1 } where
      per-class F1 = 2·tp / (2·tp + fp + fn) (algebraically 2PR/(P+R), but this form has
      a single zero-division check: 0.0 when 2·tp + fp + fn == 0).
    • Note that for MACRO, F1 is the mean of per-class F1s — not the F1 of macro-P and
      macro-R. Say so in the KDoc; it is the most common F1 implementation mistake.
  2. Factories: public fun f1Score(averaging: Averaging = Averaging.MACRO, dim: Int = -1): Metric
    and public fun binaryF1Score(threshold: Float = 0.5f): Metric.
  3. Tests in skainet-backends/skainet-backend-cpu/src/commonTest/kotlin/sk/ainet/exec/metrics/F1ScoreTest.kt,
    reference fixture pred [2,0,1,2], target [2,1,1,0]:
    • macro F1 → 0.4444 (per-class [0, 0.667, 0.667]), and assert it is not
      2·0.5·0.5/(0.5+0.5) = 0.5 — the mean-of-F1s vs. F1-of-means trap;
    • micro F1 → 0.5, equal to micro precision, micro recall and accuracy;
    • binary with threshold on 2-class logits, hand-computed;
    • a class with TP+FP+FN = 0 contributes 0.0; fresh metric compute() is 0.0;
    • accumulation across two update() calls equals one concatenated call;
    • computeForBatch (from Metric.kt) round-trips through reset().
  4. ./gradlew :skainet-lang:skainet-lang-core:apiDump and
    ./gradlew :skainet-backends:skainet-backend-cpu:jvmTest --tests "sk.ainet.exec.metrics.*".

Acceptance

  • F1Score + f1Score() + binaryF1Score() public and in the API dump
  • All test cases above pass on JVM
  • Batches are walked once per update()
  • PR body states "unit tests only; ground-truth wiring tracked in SKEEP-005 (#1223)"

Notes

Reference: sklearn f1_score docs — https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html ;
torchmetrics F1Score — https://lightning.ai/docs/torchmetrics/stable/classification/f1_score.html .
Closing this issue is what unblocks the platform-parity lane (#1230–#1233) and review (#1235).

Lenguaje dominante
Kotlin
Estrellas
52
Forks
15
Merge medio
1 d 15 h
PR fusionados (30 d)
36

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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