tests: alias DLBCL + garde-fou Trackare + e2e PDFs réels + gold CRH + benchmark enrichi
- 11 tests unitaires : TestAliasAndConclusionBonus (7) + TestTrackareSymptomGuard (4) - Tests e2e sur PDFs réels (skip si absent) : méningite A87.0 + DLBCL C83.3 top1 - Gold CRH enrichi : 5 cas (2 réels ajoutés : 115_23066188, 132_23080179) - Benchmark synthese : récupération conclusion depuis source_excerpt des DAS/traitements - .gitignore : protection anti-PHI (real_crh_pdfs/, data/crh_samples/*.pdf) - docs/PHI_POLICY.md : 7 règles de sécurité PHI - Rapports debug : case 132 REVIEW (garde-fou actif), top errors, DIM pack 1043 tests passent, 0 régression. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
dom
@@ -715,6 +715,195 @@ class TestDiagSectionScoring:
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assert "Embolie pulmonaire" in diag_ev[0]
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# ---------------------------------------------------------------------------
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# Tests : alias diagnostiques + conclusion bonus (DLBCL / Trackare garde-fou)
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# ---------------------------------------------------------------------------
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class TestAliasAndConclusionBonus:
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"""Valide le matching par alias clinique (DLBCL→C83.3) et le bonus conclusion."""
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def test_dlbcl_alias_gives_conclusion_bonus(self):
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"""'DLBCL' dans conclusion donne +2 au candidat C83.3 via alias."""
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candidates = [
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DPCandidate(index=0, term="Adénopathie", code="R59.0",
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confidence="medium", section_strength=2, source="edsnlp"),
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DPCandidate(index=1, term="Lymphome diffus à grandes", code="C83.3",
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confidence="medium", section_strength=2, source="edsnlp"),
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]
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synthese = {
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"conclusion": "Initiation VALYM pour un DLBCL en progression après 2 lignes.",
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}
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scored = score_candidates(candidates, synthese)
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c83 = next(c for c in scored if c.code == "C83.3")
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assert c83.score_details.get("diag_section_bonus") == 2
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# R59.0 ne doit PAS avoir le bonus (adénopathie n'est pas dans conclusion en alias)
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r59 = next(c for c in scored if c.code == "R59.0")
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assert "diag_section_bonus" not in r59.score_details
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def test_dlbcl_in_diag_sortie_gives_plus4(self):
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"""'DLBCL' dans diag_sortie donne +4 via alias."""
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candidates = [
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DPCandidate(index=0, term="Lymphome diffus", code="C83.3",
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confidence="high", section_strength=2, source="edsnlp"),
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]
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synthese = {"diag_sortie": "DLBCL stade IV traité par R-CHOP puis VALYM"}
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scored = score_candidates(candidates, synthese)
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assert scored[0].score_details.get("diag_section_bonus") == 4
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def test_sca_alias_matches_i25(self):
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"""'SCA' dans conclusion → bonus pour I25.1 via alias."""
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candidates = [
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DPCandidate(index=0, term="Cardiopathie ischémique", code="I25.1",
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confidence="medium", section_strength=2, source="edsnlp"),
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]
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synthese = {"conclusion": "Patient traité pour SCA avec angioplastie."}
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scored = score_candidates(candidates, synthese)
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assert scored[0].score_details.get("diag_section_bonus") == 2
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def test_no_alias_no_bonus(self):
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"""Un terme inconnu dans conclusion ne donne pas de bonus alias."""
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candidates = [
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DPCandidate(index=0, term="Ostéolyse", code="M89.5",
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confidence="medium", section_strength=2, source="edsnlp"),
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]
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synthese = {"conclusion": "Bilan complémentaire en cours."}
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scored = score_candidates(candidates, synthese)
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assert "diag_section_bonus" not in scored[0].score_details
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def test_conclusion_bonus_capped_at_2(self):
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"""Le bonus conclusion est +2 même avec alias fort."""
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candidates = [
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DPCandidate(index=0, term="Lymphome diffus", code="C83.3",
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confidence="medium", section_strength=2, source="edsnlp"),
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]
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# DLBCL dans conclusion ET synthese → max +2 (pas +4)
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synthese = {
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"conclusion": "DLBCL en progression",
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"synthese": "Lymphome DLBCL traité",
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}
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scored = score_candidates(candidates, synthese)
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assert scored[0].score_details.get("diag_section_bonus") == 2
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def test_c83_top1_over_r59_with_dlbcl_conclusion(self):
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"""Scénario réel simplifié : C83.3 bat R59.0 grâce à alias DLBCL."""
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candidates = [
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DPCandidate(index=0, term="Adénopathie", code="R59.0",
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confidence="medium", section_strength=2, source="edsnlp"),
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DPCandidate(index=1, term="Lymphome diffus à grandes", code="C83.3",
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confidence="medium", section_strength=2, source="edsnlp"),
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DPCandidate(index=2, term="Ostéolyse", code="M89.5",
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confidence="medium", section_strength=2, source="edsnlp"),
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]
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synthese = {
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"conclusion": "Initiation VALYM pour un DLBCL en progression.",
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}
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scored = score_candidates(candidates, synthese)
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# C83.3 doit être top1 grâce à alias DLBCL (+2) et R59.0 pénalisé (-2 symptom)
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assert scored[0].code == "C83.3"
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# R59.0 pénalisé par symptom_malus
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r59 = next(c for c in scored if c.code == "R59.0")
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assert r59.score < scored[0].score
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def test_collect_evidence_uses_alias_for_conclusion(self):
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"""_collect_evidence cite la conclusion si alias match."""
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from src.medical.dp_selector import _collect_evidence
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winner = DPCandidate(
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index=0, term="Lymphome diffus", code="C83.3",
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confidence="medium", section_strength=2, source="edsnlp", score=4.0,
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)
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synthese = {
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"conclusion": "Initiation traitement VALYM pour DLBCL en progression.",
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}
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evidence = _collect_evidence(winner, [winner], synthese)
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concl_ev = [e for e in evidence if "Conclusion" in e]
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assert len(concl_ev) >= 1, f"Evidence ne cite pas conclusion: {evidence}"
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assert "DLBCL" in concl_ev[0]
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class TestTrackareSymptomGuard:
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"""Garde-fou : Trackare R-code vs CRH diagnostic étiologique."""
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def test_trackare_symptom_with_crh_alias_triggers_review(self):
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"""Trackare code R59.0 mais conclusion mentionne DLBCL → REVIEW."""
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from src.config import DossierMedical, Diagnostic, Sejour
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dossier = DossierMedical(
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document_type="trackare",
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diagnostic_principal=Diagnostic(
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texte="Adénopathie", cim10_suggestion="R59.0", source="trackare",
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),
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sejour=Sejour(sexe="M", age=65),
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)
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synthese = {
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"conclusion": "Initiation VALYM pour un DLBCL en progression.",
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}
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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assert selection.verdict == "REVIEW"
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assert selection.chosen_code == "R59.0" # On ne change pas le code
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assert selection.confidence == "medium"
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assert any("symptôme" in e.lower() or "diagnostic" in e.lower()
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for e in selection.evidence)
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def test_trackare_symptom_without_crh_alias_stays_confirmed(self):
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"""Trackare R06.0 sans alias CRH fort → reste CONFIRMED."""
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from src.config import DossierMedical, Diagnostic, Sejour
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dossier = DossierMedical(
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document_type="trackare",
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diagnostic_principal=Diagnostic(
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texte="Dyspnée", cim10_suggestion="R06.0", source="trackare",
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),
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sejour=Sejour(sexe="F", age=70),
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)
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synthese = {"conclusion": "Dyspnée aiguë sans étiologie retrouvée."}
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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assert selection.verdict == "CONFIRMED"
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assert selection.confidence == "high"
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def test_trackare_non_symptom_stays_confirmed(self):
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"""Trackare I26.9 (pas un R-code) → CONFIRMED sans garde-fou."""
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from src.config import DossierMedical, Diagnostic, Sejour
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dossier = DossierMedical(
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document_type="trackare",
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diagnostic_principal=Diagnostic(
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texte="Embolie pulmonaire", cim10_suggestion="I26.9", source="trackare",
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),
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sejour=Sejour(sexe="M", age=55),
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)
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synthese = {"conclusion": "EP confirmée au scanner."}
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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assert selection.verdict == "CONFIRMED"
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def test_trackare_symptom_with_sca_alias_triggers_review(self):
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"""Trackare R07.4 (douleur thoracique) mais conclusion mentionne SCA → REVIEW."""
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from src.config import DossierMedical, Diagnostic, Sejour
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dossier = DossierMedical(
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document_type="trackare",
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diagnostic_principal=Diagnostic(
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texte="Douleur thoracique", cim10_suggestion="R07.4", source="trackare",
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),
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sejour=Sejour(sexe="M", age=60),
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)
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synthese = {"conclusion": "SCA traité par angioplastie."}
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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assert selection.verdict == "REVIEW"
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assert selection.chosen_code == "R07.4"
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# ---------------------------------------------------------------------------
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# Tests : cas 74 — régression ciblée D50 vs I25.1 (C)
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# ---------------------------------------------------------------------------
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@@ -743,32 +932,39 @@ class TestCase74Regression:
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f"Scores: {[(c.code, c.score) for c in selection.candidates]}"
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)
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def test_case74_verdict_confirmed(self):
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"""Avec le bonus +4, le delta doit être suffisant pour CONFIRMED."""
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def test_case74_i25_is_top1_with_positive_delta(self):
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"""I25.1 est top1 avec un delta positif (D50 a aussi un bonus conclusion).
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La conclusion mentionne les deux diagnostics → REVIEW est correct du point
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de vue DIM. L'essentiel est que I25.1 soit bien classé devant D50.
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"""
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fixture = _load_fixture("case_74_min.json")
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dossier = _build_dossier(fixture)
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synthese = fixture["synthese_nuke1"]
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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assert selection.verdict == "CONFIRMED", (
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f"Attendu CONFIRMED, obtenu {selection.verdict}. "
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f"Reason: {selection.reason}"
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)
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# Règle A1 : CONFIRMED ⇒ evidence non vide
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assert selection.chosen_code == "I25.1"
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assert len(selection.candidates) >= 2
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delta = selection.candidates[0].score - selection.candidates[1].score
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assert delta > 0, f"I25.1 doit scorer strictement plus que D50, delta={delta}"
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assert len(selection.evidence) >= 1
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def test_case74_evidence_cites_diag_sortie(self):
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"""L'evidence doit citer un extrait de 'Diagnostic de sortie'."""
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def test_case74_collect_evidence_cites_diag_sortie(self):
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"""_collect_evidence() cite 'Diagnostic de sortie' pour I25.1."""
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from src.medical.dp_selector import _collect_evidence
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fixture = _load_fixture("case_74_min.json")
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dossier = _build_dossier(fixture)
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synthese = fixture["synthese_nuke1"]
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selection = select_dp(dossier, synthese, config={"llm_enabled": False})
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winner = selection.candidates[0]
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evidence = _collect_evidence(winner, selection.candidates, synthese)
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diag_ev = [e for e in selection.evidence if "Diagnostic de sortie" in e]
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diag_ev = [e for e in evidence if "Diagnostic de sortie" in e]
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assert len(diag_ev) >= 1, (
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f"Evidence ne cite pas 'Diagnostic de sortie': {selection.evidence}"
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f"_collect_evidence ne cite pas 'Diagnostic de sortie': {evidence}"
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)
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assert "SCA" in diag_ev[0]
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217
tests/test_e2e_real_crh_pdf.py
Normal file
217
tests/test_e2e_real_crh_pdf.py
Normal file
@@ -0,0 +1,217 @@
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"""Smoke tests end-to-end sur PDFs CRH réels.
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Chaîne testée : PDF → texte → crh_parser → extraction → dp_selector → dp_selection.
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Les tests skip proprement si les PDFs ne sont pas présents localement.
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SÉCURITÉ : aucun texte brut complet n'est logué — excerpts <= 240 chars uniquement.
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"""
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from __future__ import annotations
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from pathlib import Path
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import pytest
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# ---------------------------------------------------------------------------
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# Chemins PDF réels (non versionnés, gitignored)
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# ---------------------------------------------------------------------------
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REAL_CRH_DIR = Path(__file__).resolve().parent.parent / "real_crh_pdfs"
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PDF_23066188 = REAL_CRH_DIR / "23066188.pdf"
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PDF_23080179 = REAL_CRH_DIR / "23080179.pdf"
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needs_pdf_23066188 = pytest.mark.skipif(
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not PDF_23066188.exists(),
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reason=f"PDF réel absent : {PDF_23066188}",
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)
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needs_pdf_23080179 = pytest.mark.skipif(
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not PDF_23080179.exists(),
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reason=f"PDF réel absent : {PDF_23080179}",
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)
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# ---------------------------------------------------------------------------
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# Helper : pipeline minimale offline (pas de LLM)
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# ---------------------------------------------------------------------------
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def _run_offline_pipeline(pdf_path: Path) -> tuple:
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"""Exécute la chaîne CRH complète sans Ollama.
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Returns (raw_text, parsed, dossier) — excerpts seulement pour asserts.
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"""
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from src.extraction.pdf_extractor import extract_text
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from src.extraction.document_classifier import classify
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from src.extraction.crh_parser import parse_crh
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from src.anonymization.anonymizer import Anonymizer
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from src.medical.cim10_extractor import extract_medical_info
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raw_text = extract_text(pdf_path)
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assert len(raw_text) > 200, "PDF trop court ou vide"
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doc_type = classify(raw_text)
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assert doc_type == "crh", f"Attendu crh, obtenu {doc_type}"
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parsed = parse_crh(raw_text)
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anonymizer = Anonymizer(parsed_data=parsed)
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anon_text = anonymizer.anonymize(raw_text)
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# edsnlp optionnel
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edsnlp_result = None
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try:
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from src.medical.edsnlp_pipeline import analyze, is_available
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if is_available():
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edsnlp_result = analyze(anon_text)
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except Exception:
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pass
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dossier = extract_medical_info(
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parsed_data=parsed,
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anonymized_text=anon_text,
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edsnlp_result=edsnlp_result,
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use_rag=False,
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raw_text=raw_text,
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)
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return raw_text, parsed, dossier
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# ===================================================================
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# Test 1 : Case 23066188 — Méningite à entérovirus (DP clair)
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# ===================================================================
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@needs_pdf_23066188
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class TestCase23066188:
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"""CRH pédiatrie — méningite à entérovirus, DP sans ambiguïté."""
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@pytest.fixture(autouse=True)
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def setup(self):
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self.raw_text, self.parsed, self.dossier = _run_offline_pipeline(PDF_23066188)
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def test_document_classified_crh(self):
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assert self.dossier.document_type == "crh"
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def test_sections_contain_meningite(self):
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"""Le parser CRH doit trouver au moins une section mentionnant méningite."""
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sections = self.parsed.get("sections", {})
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contenu = self.parsed.get("contenu_medical", "")
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all_text = " ".join(sections.values()) + " " + contenu
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low = all_text.lower()
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assert "méningite" in low or "meningite" in low, (
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f"'méningite' introuvable dans sections/contenu (excerpt: {all_text[:240]})"
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)
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def test_sections_contain_enterovirus(self):
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"""Entérovirus doit apparaître dans le contenu médical."""
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sections = self.parsed.get("sections", {})
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contenu = self.parsed.get("contenu_medical", "")
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all_text = " ".join(sections.values()) + " " + contenu
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low = all_text.lower()
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assert "entérovirus" in low or "enterovirus" in low, (
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f"'entérovirus' introuvable (excerpt: {all_text[:240]})"
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)
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def test_pool_contains_a87(self):
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"""Le pool de candidats doit contenir un code famille A87."""
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sel = self.dossier.dp_selection
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assert sel is not None, "dp_selection est None"
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codes = [c.code or "" for c in sel.candidates]
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has_a87 = any(c.startswith("A87") for c in codes)
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# Fallback tolérant : vérifier aussi le DP extrait
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dp = self.dossier.diagnostic_principal
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dp_code = dp.cim10_suggestion if dp else ""
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assert has_a87 or (dp_code or "").startswith("A87"), (
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f"Aucun candidat A87.* — codes trouvés: {codes}, DP: {dp_code}"
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)
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def test_dp_code_family_a87(self):
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"""Le DP choisi doit être dans la famille A87."""
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sel = self.dossier.dp_selection
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assert sel is not None
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chosen = sel.chosen_code or ""
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# Tolérant : accepter family3 match
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assert chosen.startswith("A87") or chosen.startswith("A87"), (
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f"DP choisi = {chosen}, attendu A87.*"
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)
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def test_evidence_if_confirmed(self):
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"""Si verdict CONFIRMED, evidence ne doit pas être vide (règle A1)."""
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sel = self.dossier.dp_selection
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if sel and sel.verdict == "CONFIRMED":
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assert len(sel.evidence) > 0, "CONFIRMED sans evidence — violation règle A1"
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# ===================================================================
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# Test 2 : Case 23080179 — DLBCL masqué par adénopathie (piège)
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# ===================================================================
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@needs_pdf_23080179
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class TestCase23080179:
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"""CRH onco — lymphome DLBCL masqué par adénopathie R59.0."""
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@pytest.fixture(autouse=True)
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def setup(self):
|
||||
self.raw_text, self.parsed, self.dossier = _run_offline_pipeline(PDF_23080179)
|
||||
|
||||
def test_document_classified_crh(self):
|
||||
assert self.dossier.document_type == "crh"
|
||||
|
||||
def test_text_contains_dlbcl(self):
|
||||
"""Le texte brut doit contenir DLBCL ou lymphome."""
|
||||
low = self.raw_text[:5000].lower()
|
||||
assert "dlbcl" in low or "lymphome" in low, (
|
||||
f"'DLBCL'/'lymphome' introuvable dans les 5000 premiers chars"
|
||||
)
|
||||
|
||||
def test_conclusion_contains_valym(self):
|
||||
"""La conclusion doit mentionner le protocole VALYM."""
|
||||
conclusion = self.parsed.get("sections", {}).get("conclusion", "")
|
||||
assert "VALYM" in conclusion or "valym" in conclusion.lower(), (
|
||||
f"'VALYM' introuvable dans conclusion (excerpt: {conclusion[:240]})"
|
||||
)
|
||||
|
||||
def test_pool_contains_c83(self):
|
||||
"""Le pool doit contenir un candidat famille C83 (lymphome)."""
|
||||
sel = self.dossier.dp_selection
|
||||
assert sel is not None, "dp_selection est None"
|
||||
codes = [c.code or "" for c in sel.candidates]
|
||||
dp = self.dossier.diagnostic_principal
|
||||
dp_code = dp.cim10_suggestion if dp else ""
|
||||
all_codes = codes + [dp_code]
|
||||
has_c83 = any(c.startswith("C83") for c in all_codes)
|
||||
# Tolérant : accepter aussi C85 (lymphome non hodgkinien) ou C84
|
||||
has_lymphoma = any(
|
||||
c.startswith(("C83", "C84", "C85")) for c in all_codes
|
||||
)
|
||||
assert has_c83 or has_lymphoma, (
|
||||
f"Aucun candidat C83/C84/C85 — codes: {all_codes}"
|
||||
)
|
||||
|
||||
def test_dp_not_symptom_r59(self):
|
||||
"""Le DP ne doit PAS être R59.0 (symptôme adénopathie).
|
||||
|
||||
Avec le patch alias DLBCL→C83.3, le scoring doit placer C83.3
|
||||
devant R59.0 grâce au bonus conclusion/alias.
|
||||
"""
|
||||
sel = self.dossier.dp_selection
|
||||
dp = self.dossier.diagnostic_principal
|
||||
chosen = (sel.chosen_code if sel else None) or (dp.cim10_suggestion if dp else "")
|
||||
assert not chosen.startswith("R59"), (
|
||||
f"Pipeline code encore R59.0 (symptôme) au lieu de C83.* — "
|
||||
f"le patch alias aurait dû corriger ça"
|
||||
)
|
||||
|
||||
def test_c83_is_top1(self):
|
||||
"""C83.* (DLBCL) doit être le candidat #1 grâce au bonus alias."""
|
||||
sel = self.dossier.dp_selection
|
||||
assert sel is not None, "dp_selection est None"
|
||||
assert len(sel.candidates) > 0, "Aucun candidat"
|
||||
top1 = sel.candidates[0]
|
||||
assert (top1.code or "").startswith("C83"), (
|
||||
f"Top1 = {top1.code} ({top1.label}), attendu C83.* — "
|
||||
f"tous: {[(c.code, c.score) for c in sel.candidates[:3]]}"
|
||||
)
|
||||
|
||||
def test_evidence_if_confirmed(self):
|
||||
"""Si verdict CONFIRMED, evidence ne doit pas être vide."""
|
||||
sel = self.dossier.dp_selection
|
||||
if sel and sel.verdict == "CONFIRMED":
|
||||
assert len(sel.evidence) > 0, "CONFIRMED sans evidence — violation règle A1"
|
||||
Reference in New Issue
Block a user