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File size: 1,765 Bytes
3358fd3 | 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | from __future__ import annotations
from typing import Any
def _clamp_score(value: Any) -> float:
try:
score = float(value)
except (TypeError, ValueError):
return 0.01
if score < 0.01:
return 0.01
if score > 0.99:
return 0.99
return score
def _extract_score_from_trajectory(trajectory: Any) -> float:
if trajectory is None:
return 0.01
if isinstance(trajectory, (int, float)):
return _clamp_score(trajectory)
if isinstance(trajectory, dict):
for key in ("score", "reward", "final_score", "final_reward"):
if key in trajectory:
return _clamp_score(trajectory.get(key))
observation = trajectory.get("observation")
if isinstance(observation, dict):
for key in ("reward", "score"):
if key in observation:
return _clamp_score(observation.get(key))
steps = trajectory.get("steps")
if isinstance(steps, list) and steps:
last_step = steps[-1]
if isinstance(last_step, dict):
for key in ("reward", "score"):
if key in last_step:
return _clamp_score(last_step.get(key))
if isinstance(trajectory, (list, tuple)) and trajectory:
return _extract_score_from_trajectory(trajectory[-1])
return 0.01
def grade_easy(trajectory: Any = None) -> float:
return _extract_score_from_trajectory(trajectory)
def grade_medium(trajectory: Any = None) -> float:
return _extract_score_from_trajectory(trajectory)
def grade_hard(trajectory: Any = None) -> float:
return _extract_score_from_trajectory(trajectory)
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