File size: 6,021 Bytes
85b19cf | 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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | """Roll up per-session and per-QA evaluations into baseline-level summaries.
Recall & correctness: per-session average (not pooled cumulative).
Interference: pooled across sessions.
QA & evidence: pooled across questions.
"""
from __future__ import annotations
from collections.abc import Mapping, Sequence
def _safe_div(a: float, b: float) -> float:
return a / b if b else 0.0
def aggregate_metrics(
baseline_id: str,
*,
session_evaluations: Sequence[Mapping[str, object]] = (),
qa_evaluations: Sequence[Mapping[str, object]] = (),
) -> dict[str, object]:
"""Aggregate all per-session and per-QA evaluations."""
# --- Per-session recall (average) ---
recall_scores: list[float] = []
update_recall_scores: list[float] = []
# --- Per-session correctness (average) ---
correctness_scores: list[float] = []
hallucination_scores: list[float] = []
irrelevant_scores: list[float] = []
# --- Update handling (pooled) ---
upd_num_updated = 0
upd_num_both = 0
upd_num_outdated = 0
upd_total_items = 0
# --- Interference rejection (pooled) ---
interf_num_rejected = 0
interf_num_memorized = 0
interf_total_items = 0
# --- Per-session detail counters (for reference) ---
total_gold_points = 0
total_covered = 0
total_memories = 0
total_correct = 0
total_hallucination = 0
total_irrelevant = 0
for s in session_evaluations:
# Recall: per-session score
r = s.get("recall")
if r is not None:
recall_scores.append(float(r))
ur = s.get("update_recall")
if ur is not None:
update_recall_scores.append(float(ur))
# Correctness: per-session score
cr = s.get("correctness_rate")
if cr is not None:
correctness_scores.append(float(cr))
nm = int(s.get("num_memories", 0))
if nm > 0:
hallucination_scores.append(
float(s.get("num_hallucination", 0)) / nm
)
irrelevant_scores.append(
float(s.get("num_irrelevant", 0)) / nm
)
# Detail counters
c = s.get("covered_count")
if c is not None:
total_covered += int(c)
total_gold_points += int(s.get("num_gold", 0))
total_memories += nm
total_correct += int(s.get("num_correct", 0))
total_hallucination += int(s.get("num_hallucination", 0))
total_irrelevant += int(s.get("num_irrelevant", 0))
# Update handling (pooled)
upd_num_updated += int(s.get("update_num_updated", 0))
upd_num_both += int(s.get("update_num_both", 0))
upd_num_outdated += int(s.get("update_num_outdated", 0))
upd_total_items += int(s.get("update_total_items", 0))
# Interference rejection (pooled)
interf_num_rejected += int(s.get("interference_num_rejected", 0))
interf_num_memorized += int(s.get("interference_num_memorized", 0))
interf_total_items += int(s.get("interference_total_items", 0))
# --- QA (pooled) ---
qa_total = 0
qa_valid = 0
qa_correct = 0
qa_hallucination = 0
qa_omission = 0
evidence_covered = 0
evidence_total = 0
for q in qa_evaluations:
qa_total += 1
label = q.get("answer_label")
if label in ("Correct", "Hallucination", "Omission"):
qa_valid += 1
if label == "Correct":
qa_correct += 1
elif label == "Hallucination":
qa_hallucination += 1
elif label == "Omission":
qa_omission += 1
ec = q.get("evidence_covered_count")
if ec is not None:
evidence_covered += int(ec)
evidence_total += int(q.get("num_evidence", 0))
n_recall = len(recall_scores)
n_update = len(update_recall_scores)
n_correct = len(correctness_scores)
n_hallu = len(hallucination_scores)
n_irrel = len(irrelevant_scores)
return {
"baseline_id": baseline_id,
"memory_recall": {
"avg_recall": _safe_div(sum(recall_scores), n_recall),
"avg_update_recall": _safe_div(sum(update_recall_scores), n_update),
"num_sessions_with_recall": n_recall,
"num_sessions_with_update": n_update,
"total_covered": total_covered,
"total_gold": total_gold_points,
},
"memory_correctness": {
"avg_correctness": _safe_div(sum(correctness_scores), n_correct),
"avg_hallucination": _safe_div(sum(hallucination_scores), n_hallu),
"avg_irrelevant": _safe_div(sum(irrelevant_scores), n_irrel),
"num_sessions": n_correct,
"total_memories": total_memories,
"total_correct": total_correct,
"total_hallucination": total_hallucination,
"total_irrelevant": total_irrelevant,
},
"update_handling": {
"score": _safe_div(upd_num_updated * 1.0 + upd_num_both * 0.5, upd_total_items),
"num_updated": upd_num_updated,
"num_both": upd_num_both,
"num_outdated": upd_num_outdated,
"num_total": upd_total_items,
},
"interference_rejection": {
"score": _safe_div(interf_num_rejected, interf_total_items),
"num_rejected": interf_num_rejected,
"num_memorized": interf_num_memorized,
"num_total": interf_total_items,
},
"question_answering": {
"correct_ratio": _safe_div(qa_correct, qa_valid),
"hallucination_ratio": _safe_div(qa_hallucination, qa_valid),
"omission_ratio": _safe_div(qa_omission, qa_valid),
"num_total": qa_total,
"num_valid": qa_valid,
},
"evidence_coverage": {
"hit_rate": _safe_div(evidence_covered, evidence_total),
"num_covered": evidence_covered,
"num_total": evidence_total,
},
}
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