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b02ffc2c99
Open-source-tool / research-code separation:
- git mv skillopt/sleep/ -> skillopt_sleep/ (top-level, sibling to the research
skillopt/ package). History preserved as renames.
- All imports skillopt.sleep.* -> skillopt_sleep.*.
- Vendor the validation gate into skillopt_sleep/gate.py (a self-contained copy
of skillopt.evaluation.gate). The engine now has ZERO dependency on the
research package — verified: grep finds no `from skillopt.` in skillopt_sleep/,
and consolidate's gate resolves to skillopt_sleep.gate.
- Plugin scripts/commands/skill call `-m skillopt_sleep`.
29 tests pass; `python -m skillopt_sleep` runs standalone.
Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
51 lines
2.1 KiB
Python
51 lines
2.1 KiB
Python
"""SkillOpt-Sleep — vendored validation gate.
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This is a self-contained copy of the SkillOpt validation gate so the sleep
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engine has ZERO dependency on the research package (skillopt/*). The research
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repo's ``skillopt.evaluation.gate`` is the reference implementation and the two
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are kept behaviourally identical; vendoring keeps this open-source tool
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decoupled from the paper's experiment code.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class GateResult:
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action: str # "accept_new_best" | "accept" | "reject"
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current_skill: str
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current_score: float
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best_skill: str
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best_score: float
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best_step: int
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def select_gate_score(hard: float, soft: float, metric: str = "hard",
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mixed_weight: float = 0.5) -> float:
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"""Project (hard, soft) onto a single comparison metric."""
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if metric == "hard":
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return float(hard)
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if metric == "soft":
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return float(soft)
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if metric == "mixed":
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w = max(0.0, min(1.0, float(mixed_weight)))
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return (1.0 - w) * float(hard) + w * float(soft)
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raise ValueError(f"unknown gate metric {metric!r}; expected hard/soft/mixed")
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def evaluate_gate(candidate_skill: str, cand_hard: float, current_skill: str,
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current_score: float, best_skill: str, best_score: float,
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best_step: int, global_step: int, *, cand_soft: float = 0.0,
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metric: str = "hard", mixed_weight: float = 0.5) -> GateResult:
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"""Pure gate decision: compare candidate score to current/best."""
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cand_score = select_gate_score(cand_hard, cand_soft, metric, mixed_weight)
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if cand_score > current_score:
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if cand_score > best_score:
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return GateResult("accept_new_best", candidate_skill, cand_score,
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candidate_skill, cand_score, global_step)
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return GateResult("accept", candidate_skill, cand_score,
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best_skill, best_score, best_step)
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return GateResult("reject", current_skill, current_score,
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best_skill, best_score, best_step)
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