778e8526d4
- 누적 작업트리 커밋: 회기 평가 복구·durable 저장, 라이브 코치 이력/근거, 교수자 학생분석, 음성 비언어 메타, PII 마스킹, 운영 티켓/헬스 등 - 문서: 완료 기록 docs/archive/ 냉동 보관, docs/ 단일 인덱스(docs/README.md)+통합 TODO(docs/TODO.md)로 정리 - 리팩터(행위 보존): Stage enum SSOT(taxonomy 소유·state_machine re-export), store recent/masked_turns 중복 제거, speaker_ko_label 단일 헬퍼, _list_sessions N+1 제거(state/turns 배치 + 턴평가 하이드레이션 배치) - 검증: 백엔드 pytest 352 passed, _list_sessions E2E chromium-single-run 2 passed
424 lines
15 KiB
Python
424 lines
15 KiB
Python
"""Phase 3 recursive-learning dataset export helpers.
|
|
|
|
The exporter is intentionally conservative: it only emits masked text, keeps raw
|
|
database identifiers out of JSONL records, and never upgrades an artifact to an
|
|
approved seed dataset unless the explicit approval and agreement gates pass.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import hashlib
|
|
import json
|
|
import math
|
|
import re
|
|
from collections import Counter, defaultdict
|
|
from dataclasses import dataclass, field
|
|
from datetime import UTC, date, datetime
|
|
from decimal import Decimal
|
|
from pathlib import Path
|
|
from typing import Any, Iterable, Mapping, Sequence
|
|
from uuid import UUID
|
|
|
|
DATASET_ITEM_SCHEMA = "phase3_dataset_item_v1"
|
|
APPROVED_EXPORT_STATUS = "approved_for_recursive_learning_seed"
|
|
DRY_RUN_EXPORT_STATUS = "technical_dry_run"
|
|
BLOCKED_EXPORT_STATUS = "blocked"
|
|
EXPORT_STATUSES = {APPROVED_EXPORT_STATUS, DRY_RUN_EXPORT_STATUS, BLOCKED_EXPORT_STATUS}
|
|
|
|
BLOCKED_FIELD_NAMES = {
|
|
"name",
|
|
"email",
|
|
"phone",
|
|
"student_id",
|
|
"national_id",
|
|
"address",
|
|
"date_of_birth",
|
|
"raw_audio_path",
|
|
"raw_voice",
|
|
"raw_source_case",
|
|
"identity_map",
|
|
"api_key",
|
|
"api_keys",
|
|
"access_token",
|
|
"refresh_token",
|
|
"token",
|
|
"cookie",
|
|
"credentials",
|
|
"credential",
|
|
"secret",
|
|
}
|
|
|
|
PII_PATTERNS: tuple[tuple[str, re.Pattern[str]], ...] = (
|
|
("email", re.compile(r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b", re.I)),
|
|
(
|
|
"phone",
|
|
re.compile(r"\b(?:\+?82[-. ]?)?(?:0?1[016789]|0[2-9]\d?)[-. ]?\d{3,4}[-. ]?\d{4}\b"),
|
|
),
|
|
("national_id", re.compile(r"\b\d{6}[- ]?[1-4]\d{6}\b")),
|
|
("student_id", re.compile(r"\b20\d{2}[- ]?\d{4,8}\b")),
|
|
(
|
|
"secret",
|
|
re.compile(
|
|
r"(?i)\b(?:api[_-]?key|access[_-]?token|refresh[_-]?token|session[_-]?secret|cookie)\b\s*[:=]\s*\S+"
|
|
),
|
|
),
|
|
("secret", re.compile(r"\b(?:sk-[A-Za-z0-9_-]{12,}|AIza[0-9A-Za-z_-]{20,}|xox[baprs]-[A-Za-z0-9-]+)\b")),
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class ExportKeyMaps:
|
|
participant: dict[str, str] = field(default_factory=dict)
|
|
session: dict[str, str] = field(default_factory=dict)
|
|
|
|
def participant_key(self, raw_id: Any) -> str:
|
|
key = str(raw_id or "unknown-participant")
|
|
if key not in self.participant:
|
|
self.participant[key] = f"PX-{len(self.participant) + 1:04d}"
|
|
return self.participant[key]
|
|
|
|
def session_key(self, raw_id: Any) -> str:
|
|
key = str(raw_id or "unknown-session")
|
|
if key not in self.session:
|
|
self.session[key] = f"SX-{len(self.session) + 1:04d}"
|
|
return self.session[key]
|
|
|
|
|
|
def json_safe(value: Any) -> Any:
|
|
if isinstance(value, (datetime, date)):
|
|
if isinstance(value, datetime) and value.tzinfo is None:
|
|
value = value.replace(tzinfo=UTC)
|
|
return value.isoformat().replace("+00:00", "Z")
|
|
if isinstance(value, (UUID, Decimal)):
|
|
return str(value)
|
|
if isinstance(value, Mapping):
|
|
return {str(key): json_safe(item) for key, item in value.items()}
|
|
if isinstance(value, list):
|
|
return [json_safe(item) for item in value]
|
|
if isinstance(value, tuple):
|
|
return [json_safe(item) for item in value]
|
|
return value
|
|
|
|
|
|
def normalize_json_value(value: Any) -> Any:
|
|
if isinstance(value, str):
|
|
stripped = value.strip()
|
|
if stripped.startswith(("{", "[")):
|
|
try:
|
|
return json.loads(stripped)
|
|
except json.JSONDecodeError:
|
|
return value
|
|
return value
|
|
|
|
|
|
def export_safe_supervisor_comments(value: Any) -> list[dict[str, Any]]:
|
|
comments = normalize_json_value(value or [])
|
|
if not isinstance(comments, list):
|
|
return []
|
|
safe_comments: list[dict[str, Any]] = []
|
|
for item in comments:
|
|
if not isinstance(item, Mapping):
|
|
continue
|
|
safe_item: dict[str, Any] = {}
|
|
for key in ("kind", "intent_deviation"):
|
|
if key in item:
|
|
safe_item[key] = json_safe(normalize_json_value(item[key]))
|
|
if safe_item:
|
|
safe_comments.append(safe_item)
|
|
return safe_comments
|
|
|
|
|
|
def _redacted_sample(kind: str, value: str) -> str:
|
|
if kind == "email" and "@" in value:
|
|
return f"<email:{value.rsplit('@', 1)[1].lower()}>"
|
|
return f"<{kind}>"
|
|
|
|
|
|
def _scan_text(value: str, path: str) -> list[dict[str, Any]]:
|
|
findings: list[dict[str, Any]] = []
|
|
for kind, pattern in PII_PATTERNS:
|
|
for match in pattern.finditer(value):
|
|
findings.append(
|
|
{
|
|
"kind": kind,
|
|
"path": path,
|
|
"sample": _redacted_sample(kind, match.group(0)),
|
|
}
|
|
)
|
|
return findings
|
|
|
|
|
|
def scan_for_pii(value: Any, path: str = "$") -> list[dict[str, Any]]:
|
|
findings: list[dict[str, Any]] = []
|
|
if isinstance(value, Mapping):
|
|
for raw_key, item in value.items():
|
|
key = str(raw_key)
|
|
next_path = f"{path}.{key}"
|
|
if key.lower() in BLOCKED_FIELD_NAMES:
|
|
findings.append({"kind": "blocked_field", "path": next_path, "sample": f"<{key.lower()}>"})
|
|
findings.extend(scan_for_pii(item, next_path))
|
|
return findings
|
|
if isinstance(value, list):
|
|
for index, item in enumerate(value):
|
|
findings.extend(scan_for_pii(item, f"{path}[{index}]"))
|
|
return findings
|
|
if isinstance(value, str):
|
|
findings.extend(_scan_text(value, path))
|
|
return findings
|
|
|
|
|
|
def build_dataset_record(
|
|
row: Mapping[str, Any],
|
|
*,
|
|
item_index: int,
|
|
export_manifest_id: str,
|
|
keys: ExportKeyMaps,
|
|
pii_scan_status: str = "pending",
|
|
consent_scope: str = "recursive_learning_seed",
|
|
) -> dict[str, Any]:
|
|
text_masked = str(row.get("text_masked") or "").strip()
|
|
if not text_masked:
|
|
raise ValueError("dataset export requires non-empty text_masked")
|
|
|
|
participant_key = keys.participant_key(row.get("learner_id"))
|
|
session_key = keys.session_key(row.get("session_id"))
|
|
persona_code = row.get("persona_code") or row.get("persona_id") or "unknown"
|
|
|
|
return {
|
|
"schema": DATASET_ITEM_SCHEMA,
|
|
"item_id": f"DI-{item_index:06d}",
|
|
"participant_key": participant_key,
|
|
"session_key": session_key,
|
|
"turn_key": f"TX-{item_index:06d}",
|
|
"persona_id": str(persona_code),
|
|
"stage": row.get("stage") or "",
|
|
"speaker": row.get("speaker") or "",
|
|
"text_masked": text_masked,
|
|
"techniques": json_safe(normalize_json_value(row.get("techniques") or [])),
|
|
"client_states": json_safe(normalize_json_value(row.get("client_states") or [])),
|
|
"feedback_scores": json_safe(normalize_json_value(row.get("feedback_scores") or [])),
|
|
"supervisor_comments": export_safe_supervisor_comments(row.get("supervisor_comments") or []),
|
|
"source_refs": {
|
|
"session_started_at": json_safe(row.get("session_started_at") or row.get("started_at")),
|
|
"export_manifest_id": export_manifest_id,
|
|
},
|
|
"privacy": {
|
|
"direct_identifiers_removed": True,
|
|
"pii_scan_status": pii_scan_status,
|
|
"consent_scope": consent_scope,
|
|
},
|
|
}
|
|
|
|
|
|
def write_jsonl(records: Sequence[Mapping[str, Any]], path: Path) -> None:
|
|
path.parent.mkdir(parents=True, exist_ok=True)
|
|
with path.open("w", encoding="utf-8", newline="\n") as handle:
|
|
for record in records:
|
|
handle.write(json.dumps(json_safe(record), ensure_ascii=False, sort_keys=True, separators=(",", ":")))
|
|
handle.write("\n")
|
|
|
|
|
|
def sha256_file(path: Path) -> str:
|
|
digest = hashlib.sha256()
|
|
with path.open("rb") as handle:
|
|
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
|
|
digest.update(chunk)
|
|
return digest.hexdigest()
|
|
|
|
|
|
def cohen_kappa(annotations: Iterable[Mapping[str, Any]], label_key: str) -> float | None:
|
|
pairs: list[tuple[Any, Any]] = []
|
|
by_item: dict[Any, list[Any]] = defaultdict(list)
|
|
for annotation in annotations:
|
|
labels = normalize_json_value(annotation.get("labels") or {})
|
|
if not isinstance(labels, Mapping) or label_key not in labels:
|
|
continue
|
|
by_item[annotation.get("item_id")].append(labels[label_key])
|
|
for values in by_item.values():
|
|
if len(values) >= 2:
|
|
pairs.append((values[0], values[1]))
|
|
if not pairs:
|
|
return None
|
|
|
|
total = len(pairs)
|
|
observed = sum(1 for left, right in pairs if left == right) / total
|
|
left_counts = Counter(left for left, _ in pairs)
|
|
right_counts = Counter(right for _, right in pairs)
|
|
expected = sum((left_counts[label] / total) * (right_counts[label] / total) for label in set(left_counts) | set(right_counts))
|
|
if math.isclose(1.0, expected):
|
|
return 1.0 if math.isclose(1.0, observed) else None
|
|
return round((observed - expected) / (1.0 - expected), 4)
|
|
|
|
|
|
def intraclass_correlation(annotations: Iterable[Mapping[str, Any]], score_key: str) -> float | None:
|
|
by_item: dict[Any, list[float]] = defaultdict(list)
|
|
for annotation in annotations:
|
|
labels = normalize_json_value(annotation.get("labels") or {})
|
|
if not isinstance(labels, Mapping) or score_key not in labels:
|
|
continue
|
|
try:
|
|
by_item[annotation.get("item_id")].append(float(labels[score_key]))
|
|
except (TypeError, ValueError):
|
|
continue
|
|
|
|
matrix = [values[:2] for values in by_item.values() if len(values) >= 2]
|
|
if len(matrix) < 2:
|
|
return None
|
|
n = len(matrix)
|
|
k = 2
|
|
row_means = [sum(row) / k for row in matrix]
|
|
col_means = [sum(row[col] for row in matrix) / n for col in range(k)]
|
|
grand_mean = sum(row_means) / n
|
|
|
|
msr = k * sum((mean - grand_mean) ** 2 for mean in row_means) / (n - 1)
|
|
msc = n * sum((mean - grand_mean) ** 2 for mean in col_means) / (k - 1)
|
|
residual = 0.0
|
|
for row_index, row in enumerate(matrix):
|
|
for col_index, value in enumerate(row):
|
|
residual += (value - row_means[row_index] - col_means[col_index] + grand_mean) ** 2
|
|
mse = residual / ((n - 1) * (k - 1))
|
|
denominator = msr + (k - 1) * mse + (k * (msc - mse) / n)
|
|
if math.isclose(denominator, 0.0):
|
|
return None
|
|
return round((msr - mse) / denominator, 4)
|
|
|
|
|
|
def infer_source_window(records: Sequence[Mapping[str, Any]]) -> dict[str, Any]:
|
|
starts = [record.get("source_refs", {}).get("session_started_at") for record in records]
|
|
starts = [value for value in starts if value]
|
|
return {
|
|
"started_at": min(starts) if starts else "",
|
|
"ended_at": max(starts) if starts else "",
|
|
}
|
|
|
|
|
|
def build_manifest(
|
|
*,
|
|
export_id: str,
|
|
dataset_name: str,
|
|
export_status: str,
|
|
purpose: str,
|
|
records: Sequence[Mapping[str, Any]],
|
|
jsonl_path: str,
|
|
jsonl_sha256: str,
|
|
pii_findings: Sequence[Mapping[str, Any]],
|
|
participants_included: int,
|
|
participants_excluded: int = 0,
|
|
cohort_id: str = "phase3",
|
|
consent_version: str = "",
|
|
agreement: Mapping[str, Any] | None = None,
|
|
approvals: Mapping[str, str] | None = None,
|
|
known_limitations: Sequence[str] | None = None,
|
|
created_at: datetime | None = None,
|
|
) -> dict[str, Any]:
|
|
if export_status not in EXPORT_STATUSES:
|
|
raise ValueError(f"unsupported export_status: {export_status}")
|
|
|
|
agreement_payload = {
|
|
"kappa": None,
|
|
"icc": None,
|
|
"gold_status": "not_gold",
|
|
}
|
|
if agreement:
|
|
agreement_payload.update(dict(agreement))
|
|
|
|
approvals_payload = {
|
|
"data_steward": "",
|
|
"legal_or_privacy_reviewer": "",
|
|
"technical_operator": "",
|
|
"approved_at": "",
|
|
}
|
|
if approvals:
|
|
approvals_payload.update({key: value for key, value in approvals.items() if value is not None})
|
|
|
|
pii_status = "pass" if not pii_findings else "fail"
|
|
limitations = list(known_limitations or [])
|
|
if export_status != APPROVED_EXPORT_STATUS:
|
|
limitations.append("technical dry-run only; data-steward/legal approval is not complete")
|
|
if pii_findings:
|
|
limitations.append("PII scan found records requiring reviewer disposition")
|
|
|
|
manifest = {
|
|
"export_id": export_id,
|
|
"dataset_name": dataset_name,
|
|
"export_status": export_status,
|
|
"created_at": json_safe(created_at or datetime.now(UTC)),
|
|
"purpose": purpose,
|
|
"source_window": infer_source_window(records),
|
|
"source_tables": [
|
|
"app.sessions",
|
|
"app.turns",
|
|
"app.feedback_scores",
|
|
"app.turn_technique",
|
|
"app.turn_client_state",
|
|
"app.supervisor_comment",
|
|
"ds.annotation",
|
|
"ds.export_manifest",
|
|
],
|
|
"selection_criteria": {
|
|
"cohort_id": cohort_id,
|
|
"min_completed_sessions": 0,
|
|
"include_withdrawn": False,
|
|
"excluded_safety_scope": ["self_harm_scenario_primary"],
|
|
},
|
|
"consent_scope": {
|
|
"consent_version": consent_version,
|
|
"allowed_uses": ["education_quality_review", "recursive_learning_seed"],
|
|
"withdrawal_cutoff_applied_at": "",
|
|
"participants_included": participants_included,
|
|
"participants_excluded": participants_excluded,
|
|
},
|
|
"anonymization": {
|
|
"participant_key": "pseudonymous export key; no identity map included",
|
|
"text_transform": "masked_text_only",
|
|
"direct_identifier_policy": "blocked",
|
|
"salt_or_identity_map_location": "not in export",
|
|
},
|
|
"pii_scan": {
|
|
"tool": "vignette.dataset_export.regex",
|
|
"version": "1",
|
|
"ran_at": json_safe(datetime.now(UTC)),
|
|
"status": pii_status,
|
|
"findings": [json_safe(finding) for finding in pii_findings],
|
|
},
|
|
"agreement": agreement_payload,
|
|
"files": [
|
|
{
|
|
"path": jsonl_path,
|
|
"rows": len(records),
|
|
"sha256": jsonl_sha256,
|
|
"schema": DATASET_ITEM_SCHEMA,
|
|
}
|
|
],
|
|
"approvals": approvals_payload,
|
|
"known_limitations": sorted(set(limitations)),
|
|
}
|
|
validate_manifest_gate(manifest)
|
|
return manifest
|
|
|
|
|
|
def validate_manifest_gate(manifest: Mapping[str, Any]) -> None:
|
|
if manifest.get("export_status") != APPROVED_EXPORT_STATUS:
|
|
return
|
|
errors: list[str] = []
|
|
pii_scan = manifest.get("pii_scan") or {}
|
|
agreement = manifest.get("agreement") or {}
|
|
approvals = manifest.get("approvals") or {}
|
|
if pii_scan.get("status") != "pass":
|
|
errors.append("PII scan must pass")
|
|
if (agreement.get("kappa") or 0) < 0.70:
|
|
errors.append("kappa must be >= 0.70")
|
|
if (agreement.get("icc") or 0) < 0.75:
|
|
errors.append("ICC must be >= 0.75")
|
|
selection_criteria = manifest.get("selection_criteria") or {}
|
|
if not isinstance(selection_criteria, Mapping) or selection_criteria.get("include_withdrawn") is not False:
|
|
errors.append("include_withdrawn must be false")
|
|
consent_scope = manifest.get("consent_scope") or {}
|
|
allowed_uses = consent_scope.get("allowed_uses") if isinstance(consent_scope, Mapping) else None
|
|
if not isinstance(allowed_uses, list) or "recursive_learning_seed" not in allowed_uses:
|
|
errors.append("recursive_learning_seed consent scope is required")
|
|
for key in ("data_steward", "legal_or_privacy_reviewer", "technical_operator", "approved_at"):
|
|
if not str(approvals.get(key) or "").strip():
|
|
errors.append(f"approval missing: {key}")
|
|
if errors:
|
|
raise ValueError("; ".join(errors))
|