from __future__ import annotations from collections import defaultdict from src.domain import ENGINE_PROFILE, EntityType, Method, PolicyAction from src.redaction import leftover_values from src.schemas import GoldSpan, Kpis, LabDocument, MethodScore, Span def iou(left: tuple[int, int], right: tuple[int, int]) -> float: start = max(left[0], right[0]) end = min(left[1], right[1]) inter = max(0, end - start) union = max(left[1], right[1]) - min(left[0], right[0]) return inter / union if union else 0.0 def _match_pairs(pred: list[Span], gold: list[GoldSpan], threshold: float = 0.5) -> tuple[int, int, int]: used: set[int] = set() tp = 0 for item in pred: if item.entity_type is EntityType.UNKNOWN: continue for index, truth in enumerate(gold): if index in used: continue if item.entity_type != truth.entity_type: continue if iou((item.start, item.end), (truth.start, truth.end)) >= threshold: used.add(index) tp += 1 break pred_n = sum(1 for item in pred if item.entity_type is not EntityType.UNKNOWN) return tp, pred_n, len(gold) def _exact_pairs(pred: list[Span], gold: list[GoldSpan]) -> tuple[int, int, int]: used: set[int] = set() tp = 0 for item in pred: if item.entity_type is EntityType.UNKNOWN: continue for index, truth in enumerate(gold): if index in used: continue if ( item.entity_type == truth.entity_type and item.start == truth.start and item.end == truth.end ): used.add(index) tp += 1 break pred_n = sum(1 for item in pred if item.entity_type is not EntityType.UNKNOWN) return tp, pred_n, len(gold) def _ratio(num: int, den: int) -> float: return num / den if den else 1.0 def over_redaction_rate(pred: list[Span], gold: list[GoldSpan], text_len: int) -> float: if text_len <= 0: return 0.0 gold_chars: set[int] = set() for item in gold: gold_chars.update(range(item.start, item.end)) extra = 0 for item in pred: if item.entity_type is EntityType.UNKNOWN: continue for pos in range(item.start, item.end): if pos not in gold_chars: extra += 1 return extra / text_len def score_document( document: LabDocument, pred: list[Span], export_text: str, export_meta: dict[str, str] | None = None, ) -> dict[str, float | int | list[str]]: tp, pred_n, gold_n = _match_pairs(pred, document.gold_spans) etp, epred, egold = _exact_pairs(pred, document.gold_spans) gold_values = [item.surface for item in document.gold_spans] + [item.surface for item in document.gold_meta] leaked = leftover_values(export_text, gold_values) meta_blob = " ".join((export_meta or {}).values()) leaked.extend(leftover_values(meta_blob, [item.surface for item in document.gold_meta])) redacted_pred = [item for item in pred if item.entity_type is not EntityType.UNKNOWN] hard_hits = leftover_values(" ".join(item.surface for item in redacted_pred), document.hard_negatives) meta_leaks = leftover_values(meta_blob, [item.surface for item in document.gold_meta]) return { "tp": tp, "pred_n": pred_n, "gold_n": gold_n, "exact_tp": etp, "exact_pred": epred, "exact_gold": egold, "over_redaction": over_redaction_rate(pred, document.gold_spans, len(document.body)), "leaked": leaked, "hard_hits": hard_hits, "meta_leaks": meta_leaks, } def aggregate_scores(method: str, rows: list[dict[str, float | int | list[str]]], n_docs: int) -> MethodScore: tp = sum(int(row["tp"]) for row in rows) pred_n = sum(int(row["pred_n"]) for row in rows) gold_n = sum(int(row["gold_n"]) for row in rows) exact_tp = sum(int(row["exact_tp"]) for row in rows) leftover = sum(1 for row in rows if row["leaked"]) meta = sum(len(row["meta_leaks"]) for row in rows) hard = sum(len(row["hard_hits"]) for row in rows) over = sum(float(row["over_redaction"]) for row in rows) / n_docs if n_docs else 0.0 residual = leftover / n_docs if n_docs else 0.0 return MethodScore( method=method, n_docs=n_docs, n_pred=pred_n, n_gold=gold_n, precision=_ratio(tp, pred_n) if pred_n else 1.0, recall=_ratio(tp, gold_n) if gold_n else 1.0, exact_precision=_ratio(exact_tp, pred_n) if pred_n else 1.0, exact_recall=_ratio(exact_tp, gold_n) if gold_n else 1.0, over_redaction=over, residual_leak=residual, leftover_exports=leftover, metadata_leaks=meta, hardneg_hits=hard, ) def per_group(documents: list[LabDocument], preds: list[list[Span]], key: str) -> dict[str, dict[str, float]]: buckets: dict[str, list[tuple[LabDocument, list[Span]]]] = defaultdict(list) for document, pred in zip(documents, preds): label = document.language.value if key == "language" else "all" if key == "type": continue buckets[label].append((document, pred)) if key == "type": types = {span.entity_type.value for doc in documents for span in doc.gold_spans} result = {} for etype in sorted(types): gold_n = pred_n = tp = 0 for document, pred in zip(documents, preds): gold = [span for span in document.gold_spans if span.entity_type.value == etype] chosen = [span for span in pred if span.entity_type.value == etype] hit, pn, gn = _match_pairs(chosen, gold) tp += hit pred_n += pn gold_n += gn result[etype] = {"precision": _ratio(tp, pred_n) if pred_n else 1.0, "recall": _ratio(tp, gold_n) if gold_n else 1.0} return result result = {} for label, pairs in buckets.items(): tp = pred_n = gold_n = 0 for document, pred in pairs: hit, pn, gn = _match_pairs(pred, document.gold_spans) tp += hit pred_n += pn gold_n += gn result[label] = {"precision": _ratio(tp, pred_n) if pred_n else 1.0, "recall": _ratio(tp, gold_n) if gold_n else 1.0} return result def kpis_from( n_docs: int, policy: MethodScore, baseline: MethodScore, n_spans: int, n_review: int, n_redacted: int, elapsed_ms: int, pages: int, chars: int, action: PolicyAction, ) -> Kpis: return Kpis( n_docs=n_docs, n_spans=n_spans, n_review=n_review, n_redacted=n_redacted, policy_precision=policy.precision, policy_recall=policy.recall, baseline_precision=baseline.precision, baseline_recall=baseline.recall, residual_leak=policy.residual_leak, over_redaction=policy.over_redaction, leftover_exports=policy.leftover_exports, metadata_leaks=policy.metadata_leaks, elapsed_ms=elapsed_ms, engine_profile=ENGINE_PROFILE, policy_action=action.value, pages=pages, chars=chars, ) def comparison_rows(policy: MethodScore, baseline: MethodScore) -> list[dict[str, float | int | str]]: return [ { "method": item.method, "precision": round(item.precision, 3), "recall": round(item.recall, 3), "over_redaction": round(item.over_redaction, 4), "residual_leak": round(item.residual_leak, 3), "leftover_exports": item.leftover_exports, "metadata_leaks": item.metadata_leaks, "hardneg_hits": item.hardneg_hits, } for item in (baseline, policy) ]