mirror of
https://github.com/microsoft/SkillOpt.git
synced 2026-07-03 22:24:36 +08:00
41be2f1803
int() truncates smoothed composite scores (0.0-1.0) to 0, making all continuous reward values appear as failures. This broke SkillOpt training pipelines using SmoothedCompositeReward.
29 lines
920 B
Python
29 lines
920 B
Python
"""Scoring and hashing utilities."""
|
|
from __future__ import annotations
|
|
|
|
import hashlib
|
|
|
|
|
|
def compute_score(results: list) -> tuple[float, float]:
|
|
"""Compute hard and soft accuracy from a list of episode results.
|
|
|
|
Accepts both plain dicts and :class: instances. hard may be continuous (0.0-1.0) when using smoothed reward.
|
|
"""
|
|
if not results:
|
|
return 0.0, 0.0
|
|
|
|
def _hard(r: object) -> float:
|
|
return float(r.hard if hasattr(r, "hard") else r.get("hard", 0))
|
|
|
|
def _soft(r: object) -> float:
|
|
return float(r.soft if hasattr(r, "soft") else r.get("soft", 0.0))
|
|
|
|
hard = sum(_hard(r) for r in results) / len(results)
|
|
soft = sum(_soft(r) for r in results) / len(results)
|
|
return hard, soft
|
|
|
|
|
|
def skill_hash(content: str) -> str:
|
|
"""Return a short deterministic hash of skill content (for caching)."""
|
|
return hashlib.sha256(content.encode()).hexdigest()[:16]
|