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65d9862 46bb905 65d9862 9011070 65d9862 d109222 9011070 65d9862 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | """Tests Sprint 75 โ A.I.4 chantier 1 : co-occurrence taxonomique.
Couvre :
1. ``compute_taxonomy_cooccurrence`` :
- Matrice symรฉtrique
- Diagonale = 1.0 pour classes prรฉsentes
- Classes toujours ensemble โ Jaccard = 1
- Classes jamais ensemble โ Jaccard = 0
- Cas dรฉgรฉnรฉrรฉ : per_doc_classes vide โ None
- ``min_doc_count`` filtre les classes anecdotiques
- ``top_pairs`` triรฉes par Jaccard descendant
2. Rendu HTML :
- SVG bien formรฉ
- Table top_pairs prรฉsente
- Cellules colorรฉes
- ``""`` si ``data is None``
3. Anti-injection : noms de classes contenant ``<script>``.
4. Complรฉtude i18n FR/EN.
"""
from __future__ import annotations
import json
from pathlib import Path
import pytest
from picarones.evaluation.metrics.taxonomy_cooccurrence import (
compute_taxonomy_cooccurrence,
)
from picarones.reports.html.renderers.taxonomy_cooccurrence import (
build_taxonomy_cooccurrence_html,
)
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# 1. Couche de calcul
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
class TestCompute:
def test_classes_always_together_jaccard_one(self) -> None:
# 5 docs, A et B toujours ensemble
per_doc = [
{"A", "B"}, {"A", "B"}, {"A", "B"},
{"A", "B"}, {"A", "B"},
]
result = compute_taxonomy_cooccurrence(per_doc)
assert result is not None
assert result["cooccurrence_matrix"]["A"]["B"] == 1.0
def test_classes_never_together_jaccard_zero(self) -> None:
# A et B mutuellement exclusifs
per_doc = [
{"A"}, {"A"}, {"B"}, {"B"}, {"B"},
]
result = compute_taxonomy_cooccurrence(per_doc)
assert result is not None
assert result["cooccurrence_matrix"]["A"]["B"] == 0.0
def test_diagonal_is_one(self) -> None:
per_doc = [{"A"}, {"B"}, {"A", "B"}]
result = compute_taxonomy_cooccurrence(per_doc)
assert result["cooccurrence_matrix"]["A"]["A"] == 1.0
assert result["cooccurrence_matrix"]["B"]["B"] == 1.0
def test_symmetric(self) -> None:
per_doc = [{"A", "B"}, {"A"}, {"B"}, {"A", "B"}]
result = compute_taxonomy_cooccurrence(per_doc)
assert result["cooccurrence_matrix"]["A"]["B"] == \
result["cooccurrence_matrix"]["B"]["A"]
def test_partial_overlap(self) -> None:
# 4 docs : AโชB = 4, AโฉB = 2 โ Jaccard = 0.5
per_doc = [{"A", "B"}, {"A", "B"}, {"A"}, {"B"}]
result = compute_taxonomy_cooccurrence(per_doc)
assert result["cooccurrence_matrix"]["A"]["B"] == pytest.approx(0.5)
def test_empty_corpus_returns_none(self) -> None:
assert compute_taxonomy_cooccurrence([]) is None
assert compute_taxonomy_cooccurrence([set(), set()]) is None
def test_min_doc_count_filter(self) -> None:
# A apparaรฎt 5 fois, B apparaรฎt 1 fois (anecdotique)
per_doc = [{"A", "B"}, {"A"}, {"A"}, {"A"}, {"A"}]
result = compute_taxonomy_cooccurrence(per_doc, min_doc_count=2)
assert result is not None
assert "A" in result["classes"]
assert "B" not in result["classes"]
def test_top_pairs_sorted(self) -> None:
per_doc = [
{"A", "B", "C"}, # 3 ensemble
{"A", "B"}, # AB
{"A", "B"}, # AB
{"C"}, # C seul
]
result = compute_taxonomy_cooccurrence(per_doc, top_n_pairs=10)
assert result is not None
top = result["top_pairs"]
# Triรฉes par Jaccard dรฉcroissant
for i in range(len(top) - 1):
assert top[i][2] >= top[i + 1][2]
def test_top_pairs_count_limit(self) -> None:
per_doc = [{"A", "B", "C", "D"}]
result = compute_taxonomy_cooccurrence(per_doc, top_n_pairs=2)
assert len(result["top_pairs"]) == 2
def test_doc_count_correct(self) -> None:
per_doc = [{"A"}, {"A", "B"}, {"B"}]
result = compute_taxonomy_cooccurrence(per_doc)
assert result["doc_count"]["A"] == 2
assert result["doc_count"]["B"] == 2
assert result["n_documents"] == 3
def test_none_doc_skipped(self) -> None:
per_doc = [{"A"}, None, {"B"}]
result = compute_taxonomy_cooccurrence(per_doc)
assert result is not None
assert result["n_documents"] == 2
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# 2. Rendu HTML
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
class TestRender:
def test_returns_empty_when_data_none(self) -> None:
assert build_taxonomy_cooccurrence_html(None) == ""
def test_returns_empty_when_classes_empty(self) -> None:
data = {"classes": [], "cooccurrence_matrix": {},
"top_pairs": [], "n_documents": 0}
assert build_taxonomy_cooccurrence_html(data) == ""
def test_renders_svg(self) -> None:
per_doc = [{"A", "B"}, {"A"}, {"B"}]
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
assert "<svg" in html
assert "</svg>" in html
assert "Co-occurrence" in html
def test_renders_top_pairs_table(self) -> None:
per_doc = [{"A", "B"}, {"A", "B"}, {"A"}]
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
# Table avec en-tรชtes Paire + Jaccard
assert "Paire" in html
assert "Jaccard" in html
# Au moins une cellule de valeur Jaccard
assert "0." in html or "1." in html
def test_jaccard_values_displayed(self) -> None:
per_doc = [{"A", "B"}] * 5 # toujours ensemble โ 1.0
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
assert "1.00" in html
def test_class_labels_present(self) -> None:
per_doc = [{"ligature_error", "abbreviation_error"}, {"ligature_error"}]
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
assert "ligature_error" in html
assert "abbreviation_error" in html
def test_n_docs_displayed(self) -> None:
per_doc = [{"A", "B"}, {"A"}, {"B"}]
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
assert "3" in html # n_documents = 3
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# 3. Anti-injection
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
class TestAntiInjection:
def test_class_name_with_script_escaped(self) -> None:
per_doc = [{"<script>", "B"}, {"<script>"}]
data = compute_taxonomy_cooccurrence(per_doc)
html = build_taxonomy_cooccurrence_html(data)
assert "<script>" not in html.replace(
"<script>alert", "@@@", # ne devrait pas รชtre prรฉsent de toute faรงon
)
assert "<script>" in html
def test_label_via_i18n_escaped(self) -> None:
per_doc = [{"A", "B"}]
data = compute_taxonomy_cooccurrence(per_doc)
labels = {"taxocooc_title": "<b>Hack</b>"}
html = build_taxonomy_cooccurrence_html(data, labels=labels)
assert "<b>Hack</b>" not in html
assert "<b>Hack</b>" in html
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# 4. Complรฉtude i18n
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
class TestI18nCompleteness:
def _load(self, lang: str) -> dict:
path = (
Path(__file__).parent.parent.parent
/ "picarones" / "reports" / "i18n" / f"{lang}.json"
)
return json.loads(path.read_text(encoding="utf-8"))
def test_all_keys_fr(self) -> None:
d = self._load("fr")
for key in (
"taxocooc_title", "taxocooc_note", "taxocooc_n_docs",
"taxocooc_pair_label", "taxocooc_jaccard_label",
):
assert key in d, f"manque clรฉ FR : {key}"
def test_all_keys_en(self) -> None:
d_fr = self._load("fr")
d_en = self._load("en")
for key in d_fr:
if key.startswith("taxocooc_"):
assert key in d_en, f"manque clรฉ EN : {key}"
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