File size: 2,026 Bytes
b0c62b4 bd601e0 b0c62b4 bd601e0 b0c62b4 bd601e0 b0c62b4 bd601e0 | 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 61 62 | from __future__ import annotations
from typing import Iterable
try:
from models import AppObservation
except ImportError:
from app.models import AppObservation
def _clamp_score(value):
return max(0.0, min(1.0, float(value)))
def _safe_ratio(numerator, denominator):
if denominator <= 0:
return 0.0
return _clamp_score(numerator / denominator)
def _attempt_quality(rewards):
rewards = list(rewards)
if not rewards:
return 0.0
positive_attempts = sum(1 for reward in rewards if reward > 0)
return _safe_ratio(positive_attempts, len(rewards))
def feedback(score, category):
if score >= 0.9:
return f"{category} performance is excellent and highly reliable."
if score >= 0.7:
return f"{category} performance is solid with minor room for improvement."
if score >= 0.4:
return f"{category} performance is partial and needs more consistency."
return f"{category} performance is weak and needs significant improvement."
def grade_segmentation(app_obs: AppObservation):
total_objects = len(app_obs.objectsFound) + len(app_obs.objectsLeft)
progress_score = _safe_ratio(len(app_obs.objectsFound), total_objects)
quality_score = _attempt_quality(app_obs.rewardListSegment)
score = _clamp_score((progress_score * 0.7) + (quality_score * 0.3))
return score, feedback(score, "Segmentation")
def grade_placement(app_obs: AppObservation):
total_objects = len(app_obs.objectsFound) + len(app_obs.objectsLeft)
progress_score = _safe_ratio(app_obs.numberPlaced, total_objects)
quality_score = _attempt_quality(app_obs.rewardListPlace)
score = _clamp_score((progress_score * 0.7) + (quality_score * 0.3))
return score, feedback(score, "Placement")
def grade_adjustment(app_obs: AppObservation):
rewards = list(app_obs.rewardListAdjust)
if not rewards:
return 0.0, "Adjustment was not attempted."
score = _attempt_quality(rewards)
return score, feedback(score, "Adjustment")
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