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Agent graders for each task difficulty level.
All graders return a float score in [0.0, 1.0].
score_action() grades pure label correctness (priority/category/route/summary/escalation).
Sequential penalties (SLA, budget, queue) are applied by environment.py, not here.
grade_episode() aggregates label scores across a full episode for reporting.
"""
from __future__ import annotations
import re
from typing import Any, Dict, List, Tuple
from models import Action, Priority, RouteTo, RewardBreakdown, Reward
from dataset import ALL_EMAILS_BY_ID
PRIORITY_WEIGHT = 0.35
CATEGORY_WEIGHT = 0.25
ROUTING_WEIGHT = 0.25
SUMMARY_WEIGHT = 0.10
ESCALATION_WEIGHT = 0.05
PRIORITY_ADJACENCY: Dict[str, Dict[str, float]] = {
"urgent": {"urgent":1.0,"high":0.5,"medium":0.1,"low":0.0,"spam":0.0},
"high": {"urgent":0.5,"high":1.0,"medium":0.5,"low":0.1,"spam":0.0},
"medium": {"urgent":0.1,"high":0.5,"medium":1.0,"low":0.5,"spam":0.0},
"low": {"urgent":0.0,"high":0.1,"medium":0.5,"low":1.0,"spam":0.2},
"spam": {"urgent":0.0,"high":0.0,"medium":0.0,"low":0.2,"spam":1.0},
}
RELATED_CATEGORIES: Dict[Tuple[str,str], float] = {
("customer_complaint","billing_inquiry"):0.4,
("billing_inquiry","customer_complaint"):0.4,
("legal_compliance","customer_complaint"):0.2,
("customer_complaint","legal_compliance"):0.2,
("technical_support","customer_complaint"):0.3,
("customer_complaint","technical_support"):0.3,
("internal_hr","legal_compliance"):0.2,
("legal_compliance","internal_hr"):0.2,
("general_inquiry","billing_inquiry"):0.3,
}
def _category_score(predicted: str, actual: str) -> float:
if predicted == actual:
return 1.0
return RELATED_CATEGORIES.get((predicted, actual), 0.0)
def _routing_score(predicted: str, actual: str) -> float:
if predicted == actual:
return 1.0
acceptable = {
"support_tier2": ["support_tier1"],
"support_tier1": ["support_tier2"],
"management": ["legal"],
"legal": ["management"],
"hr": ["management"],
"trash": ["archive"],
"archive": ["trash"],
}
if predicted in acceptable.get(actual, []):
return 0.4
return 0.0
def _summary_score(summary: str, body: str, subject: str) -> float:
if not summary or len(summary) < 10:
return 0.0
score = 0.0
if 30 <= len(summary) <= 280:
score += 0.4
if summary.strip().lower() != subject.strip().lower():
score += 0.2
body_words = set(re.findall(r'\b\w{5,}\b', body.lower()))
summ_words = set(re.findall(r'\b\w{5,}\b', summary.lower()))
overlap = body_words & summ_words
score += 0.3 if len(overlap) >= 3 else (0.1 if overlap else 0.0)
if not summary.strip().startswith("I "):
score += 0.1
return min(score, 1.0)
def score_action(action: Action) -> Tuple[Reward, Dict[str, Any]]:
"""
Grade label correctness for one action. Returns (Reward, detail).
Sequential penalties (SLA, budget, queue) are NOT included here —
they are added by EmailTriageEnv.step().
"""
email_data = ALL_EMAILS_BY_ID.get(action.email_id)
if email_data is None:
bd = RewardBreakdown(base_penalty=-0.5)
return Reward(total=-0.5, breakdown=bd,
info={"error": f"Unknown email_id: {action.email_id}"}), {"error":"unknown_id"}
gt = email_data["ground_truth"]
email = email_data["email"]
subject = email["header"]["subject"]
body = email["body"]
pri = PRIORITY_ADJACENCY.get(gt["priority"], {}).get(action.priority.value, 0.0)
cat = _category_score(action.category.value, gt["category"])
rte = _routing_score(action.route_to.value, gt["route_to"])
summ = _summary_score(action.summary, body, subject)
esc_needed = gt.get("requires_escalation", False)
if action.flag_review == esc_needed:
esc = 1.0
elif action.flag_review and not esc_needed:
esc = 0.5
else:
esc = 0.0
base_penalty = 0.0
if gt["category"] == "spam_phishing" and action.route_to not in (RouteTo.TRASH, RouteTo.ARCHIVE):
base_penalty -= 0.2
if gt["priority"] == "urgent" and action.priority == Priority.SPAM:
base_penalty -= 0.3
bd = RewardBreakdown(
priority_score = pri,
category_score = cat,
routing_score = rte,
summary_score = summ,
escalation_score = esc,
base_penalty = base_penalty,
)
total = max(0.0, min(1.0,
pri*PRIORITY_WEIGHT + cat*CATEGORY_WEIGHT + rte*ROUTING_WEIGHT
+ summ*SUMMARY_WEIGHT + esc*ESCALATION_WEIGHT + base_penalty
))
detail = {
"email_id": action.email_id,
"gt_priority": gt["priority"],
"gt_category": gt["category"],
"gt_route": gt["route_to"],
"gt_escalate": esc_needed,
"pred_priority": action.priority.value,
"pred_category": action.category.value,
"pred_route": action.route_to.value,
"pred_escalate": action.flag_review,
"scores": {
"priority": round(pri, 3),
"category": round(cat, 3),
"routing": round(rte, 3),
"summary": round(summ, 3),
"escalation": round(esc, 3),
"base_penalty": round(base_penalty, 3),
},
"label_total": round(total, 3),
}
return Reward(total=total, breakdown=bd, info=detail), detail
def grade_episode(actions: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Aggregate label-correctness scores across a full episode.
Note: this does NOT include sequential penalties (SLA/budget/queue) —
use the per-step rewards from env.step() for the full picture.
"""
per_email: List[Dict] = []
totals: List[float] = []
for a_dict in actions:
try:
action = Action(**a_dict)
reward, detail = score_action(action)
per_email.append(detail)
totals.append(reward.total)
except Exception as exc:
per_email.append({"error": str(exc), "total": 0.0})
totals.append(0.0)
overall = sum(totals) / len(totals) if totals else 0.0
return {
"label_score": round(overall, 4), # label correctness only
"num_emails": len(totals),
"per_email_scores": per_email,
"min_score": round(min(totals), 4) if totals else 0.0,
"max_score": round(max(totals), 4) if totals else 0.0,
}
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