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388e3f2 | 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 | """Rendu HTML du recall sur tokens rares (Sprint 71, A.I.1).
Petit tableau récapitulatif moteur × {n_rare_tokens, n_recalled,
recall, n_docs}. Adaptive : retourne ``""`` si aucune donnée.
Critique pour l'indexation prosopographique : un OCR qui rate
systématiquement les noms propres rares produit un corpus
inutilisable pour la recherche, même avec un CER global respectable.
"""
from __future__ import annotations
from html import escape as _e
from typing import Optional
from picarones.report.render_helpers import color_traffic_light
def build_rare_token_recall_html(
per_engine: Optional[dict[str, dict]],
labels: Optional[dict[str, str]] = None,
) -> str:
"""Construit le tableau récapitulatif du recall sur tokens rares.
Parameters
----------
per_engine:
Sortie de
:func:`picarones.report.report_data.extra_metrics.compute_rare_token_recall_per_engine`.
Dict ``{engine_name: {n_rare_tokens, n_recalled, recall, n_docs, max_freq}}``.
Si ``None`` ou vide, retourne ``""``.
labels:
Dict i18n optionnel.
"""
if not per_engine:
return ""
labels = labels or {}
title = labels.get(
"rare_token_title", "Recall sur tokens rares (hapax + dis legomena)",
)
note = labels.get(
"rare_token_note",
"Pour chaque moteur, fraction des tokens rares (apparaissant ≤ 2 "
"fois dans la GT du corpus) effectivement transcrits. Critique "
"pour l'indexation prosopographique — un OCR qui rate les noms "
"propres rares rend le corpus inutilisable pour la recherche.",
)
h_engine = labels.get("rare_token_engine", "Moteur")
h_recall = labels.get("rare_token_recall", "Recall")
h_recalled = labels.get("rare_token_recalled", "Tokens recalled")
h_total = labels.get("rare_token_total", "Tokens rares (corpus)")
h_docs = labels.get("rare_token_docs", "Docs évalués")
rows = [
(engine, info)
for engine, info in per_engine.items()
if isinstance(info, dict)
]
if not rows:
return ""
parts = [
'<section class="rare-token-section" style="margin:1rem 0">',
f'<h3 style="margin:0 0 .3rem 0">{_e(title)}</h3>',
f'<div style="font-size:.85rem;opacity:.75;margin-bottom:.5rem">'
f'{_e(note)}</div>',
'<table style="border-collapse:collapse;width:100%;'
'font-size:.9rem">',
'<thead><tr>',
]
for h in (h_engine, h_recall, h_recalled, h_total, h_docs):
parts.append(
f'<th scope="col" style="padding:.4rem .6rem;text-align:left;'
f'border-bottom:1px solid #ccc;font-weight:600">{_e(h)}</th>'
)
parts.append('</tr></thead><tbody>')
# Tri par recall décroissant (les meilleurs en haut, None en queue).
sorted_rows = sorted(
rows,
key=lambda kv: -(kv[1].get("recall") or -1.0),
)
for engine, info in sorted_rows:
recall = info.get("recall")
n_recalled = int(info.get("n_recalled") or 0)
n_total = int(info.get("n_rare_tokens") or 0)
n_docs = int(info.get("n_docs") or 0)
if isinstance(recall, (int, float)):
recall_color = color_traffic_light(float(recall))
recall_cell = (
f'<td style="padding:.4rem .6rem;text-align:right;'
f'background:{recall_color};font-family:monospace;'
f'font-weight:600">{recall * 100:.1f} %</td>'
)
else:
recall_cell = (
'<td style="padding:.4rem .6rem;text-align:right;'
'opacity:.4">—</td>'
)
parts.append(
f'<tr>'
f'<td style="padding:.4rem .6rem">{_e(str(engine))}</td>'
f'{recall_cell}'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{n_recalled}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{n_total}</td>'
f'<td style="padding:.4rem .6rem;text-align:right;'
f'font-family:monospace">{n_docs}</td>'
f'</tr>'
)
parts.append('</tbody></table></section>')
return "".join(parts)
__all__ = ["build_rare_token_recall_html"]
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