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019e7db 989722c a10fe96 989722c 019e7db a10fe96 019e7db | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | """Reward shaping logic for RL-ready code analysis scores."""
from __future__ import annotations
from schemas.response import ScoreBreakdown
class RewardService:
"""Compute reward scores from model, domain, lint, and complexity signals."""
@staticmethod
def _clamp_score(value: float) -> float:
return round(max(0.01, min(0.99, float(value))), 4)
def compute(self, *, ml_score: float, domain_score: float, lint_score: float, complexity_penalty: float) -> ScoreBreakdown:
"""Apply dynamic reward shaping based on quality, errors, and completion."""
quality_signal = max(0.0, min(1.0, (0.45 * ml_score) + (0.3 * domain_score) + (0.25 * lint_score)))
error_reduction_signal = max(0.0, min(1.0, lint_score - (0.6 * complexity_penalty)))
completion_signal = max(0.0, min(1.0, (ml_score + domain_score + lint_score) / 3.0))
reward = max(
0.0,
min(
1.0,
(0.35 * quality_signal)
+ (0.25 * completion_signal)
+ (0.2 * error_reduction_signal)
+ (0.1 * ml_score)
+ (0.1 * domain_score)
- (0.15 * complexity_penalty),
),
)
return ScoreBreakdown(
ml_score=self._clamp_score(ml_score),
domain_score=self._clamp_score(domain_score),
lint_score=self._clamp_score(lint_score),
complexity_penalty=self._clamp_score(complexity_penalty),
quality_signal=self._clamp_score(quality_signal),
error_reduction_signal=self._clamp_score(error_reduction_signal),
completion_signal=self._clamp_score(completion_signal),
reward=self._clamp_score(reward),
)
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