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https://github.com/microsoft/SkillOpt.git
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244e346b83
- Skill optimization framework with training loop analogy - 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen) - WebUI for browser-based training control - Pluggable architecture for extending benchmarks and backends
30 lines
942 B
Python
30 lines
942 B
Python
"""Scoring and hashing utilities."""
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from __future__ import annotations
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import hashlib
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def compute_score(results: list) -> tuple[float, float]:
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"""Compute hard and soft accuracy from a list of episode results.
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Accepts both plain dicts and :class:`~skillopt.types.RolloutResult`
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instances.
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"""
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if not results:
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return 0.0, 0.0
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def _hard(r: object) -> int:
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return int(r.hard if hasattr(r, "hard") else r.get("hard", 0)) # type: ignore[union-attr]
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def _soft(r: object) -> float:
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return float(r.soft if hasattr(r, "soft") else r.get("soft", 0.0)) # type: ignore[union-attr]
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hard = sum(_hard(r) for r in results) / len(results)
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soft = sum(_soft(r) for r in results) / len(results)
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return hard, soft
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def skill_hash(content: str) -> str:
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"""Return a short deterministic hash of skill content (for caching)."""
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return hashlib.sha256(content.encode()).hexdigest()[:16]
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