From 0ba5424eb0d648811de22880f92d85ebc1c82220 Mon Sep 17 00:00:00 2001 From: Domi31tls Date: Mon, 2 Mar 2026 10:51:38 +0100 Subject: [PATCH] =?UTF-8?q?feat:=20Annotation=20automatique=20et=20=C3=A9v?= =?UTF-8?q?aluation=20qualit=C3=A9=20baseline=20-=20Rappel=20100%,=20Pr?= =?UTF-8?q?=C3=A9cision=2018.97%?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tasks.md | 12 +- .../001_simple_unknown_BACTERIO_23018396.json | 31 + ..._simple_unknown_bacterio_476_23159413.json | 39 + .../003_simple_compte_rendu_CRO_23155084.json | 25 + ...h_anapath_53_23224186.redacted_raster.json | 7 + .../005_simple_compte_rendu_CRH_23155836.json | 118 + ...e-14004105-23202435_14004105_23202435.json | 43 + .../009_simple_compte_rendu_CRO_23051225.json | 32 + .../010_simple_anapath_ANAPATH_23217289.json | 35 + .../011_moyen_compte_rendu_CRH_23080179.json | 59 + ...2_moyen_compte_rendu_CRH_692_23200418.json | 53 + ...3_moyen_compte_rendu_363_23085243_CRO.json | 57 + ...te_rendu_CRO_23167029.redacted_raster.json | 7 + ...wn_CONSULTATION_ANESTHESISTE_23139653.json | 38 + .../016_moyen_compte_rendu_CRH_23149905.json | 194 ++ ...te_rendu_CRO_23222062.redacted_raster.json | 7 + .../018_moyen_compte_rendu_CRH_23042753.json | 227 ++ ...9_moyen_compte_rendu_CRO_332_23049003.json | 95 + ...te_rendu_CRO_23084754.redacted_raster.json | 7 + ...te_rendu_CRO_23201117.redacted_raster.json | 7 + ..._moyen_compte_rendu_cro2_516_23187028.json | 23 + ...23_complexe_compte_rendu_CRH_23102610.json | 570 +++++ ...e-17001141-23066188_17001141_23066188.json | 219 ++ ...e-02016820-23095226_02016820_23095226.json | 368 ++++ ...e-15000536-23074384_15000536_23074384.json | 303 +++ ...e-10027557-23183041_10027557_23183041.json | 235 ++ .../annotations/dataset_statistics.json | 23 + ...unknown_BACTERIO_23018396.annotations.json | 55 + ...own_bacterio_476_23159413.annotations.json | 60 + ...compte_rendu_CRO_23155084.annotations.json | 25 + ..._23224186.redacted_raster.annotations.json | 4 + ...compte_rendu_CRH_23155836.annotations.json | 315 +++ ...3202435_14004105_23202435.annotations.json | 85 + ...compte_rendu_CRO_23051225.annotations.json | 45 + ..._anapath_ANAPATH_23217289.annotations.json | 75 + ...compte_rendu_CRH_23080179.annotations.json | 70 + ...te_rendu_CRH_692_23200418.annotations.json | 155 ++ ...te_rendu_363_23085243_CRO.annotations.json | 165 ++ ..._23167029.redacted_raster.annotations.json | 4 + ...ION_ANESTHESISTE_23139653.annotations.json | 40 + ...compte_rendu_CRH_23149905.annotations.json | 580 +++++ ..._23222062.redacted_raster.annotations.json | 4 + ...compte_rendu_CRH_23042753.annotations.json | 620 ++++++ ...te_rendu_CRO_332_23049003.annotations.json | 280 +++ ..._23084754.redacted_raster.annotations.json | 4 + ..._23201117.redacted_raster.annotations.json | 4 + ...e_rendu_cro2_516_23187028.annotations.json | 25 + ...compte_rendu_CRH_23102610.annotations.json | 1900 +++++++++++++++++ ...3066188_17001141_23066188.annotations.json | 275 +++ ...3095226_02016820_23095226.annotations.json | 490 +++++ ...3074384_15000536_23074384.annotations.json | 405 ++++ ...3183041_10027557_23183041.annotations.json | 270 +++ .../baseline_quality_evaluation.json | 441 ++++ tools/auto_annotate_dataset.py | 238 +++ tools/convert_annotations_format.py | 77 + tools/run_quality_evaluation.py | 231 ++ 56 files changed, 9770 insertions(+), 6 deletions(-) create mode 100644 tests/ground_truth/annotations/001_simple_unknown_BACTERIO_23018396.json create mode 100644 tests/ground_truth/annotations/002_simple_unknown_bacterio_476_23159413.json create mode 100644 tests/ground_truth/annotations/003_simple_compte_rendu_CRO_23155084.json create mode 100644 tests/ground_truth/annotations/004_simple_anapath_anapath_53_23224186.redacted_raster.json create mode 100644 tests/ground_truth/annotations/005_simple_compte_rendu_CRH_23155836.json create mode 100644 tests/ground_truth/annotations/008_simple_trackare_trackare-14004105-23202435_14004105_23202435.json create mode 100644 tests/ground_truth/annotations/009_simple_compte_rendu_CRO_23051225.json create mode 100644 tests/ground_truth/annotations/010_simple_anapath_ANAPATH_23217289.json create mode 100644 tests/ground_truth/annotations/011_moyen_compte_rendu_CRH_23080179.json create mode 100644 tests/ground_truth/annotations/012_moyen_compte_rendu_CRH_692_23200418.json create mode 100644 tests/ground_truth/annotations/013_moyen_compte_rendu_363_23085243_CRO.json create mode 100644 tests/ground_truth/annotations/014_moyen_compte_rendu_CRO_23167029.redacted_raster.json create mode 100644 tests/ground_truth/annotations/015_moyen_unknown_CONSULTATION_ANESTHESISTE_23139653.json create mode 100644 tests/ground_truth/annotations/016_moyen_compte_rendu_CRH_23149905.json create mode 100644 tests/ground_truth/annotations/017_moyen_compte_rendu_CRO_23222062.redacted_raster.json create mode 100644 tests/ground_truth/annotations/018_moyen_compte_rendu_CRH_23042753.json create mode 100644 tests/ground_truth/annotations/019_moyen_compte_rendu_CRO_332_23049003.json create mode 100644 tests/ground_truth/annotations/020_moyen_compte_rendu_CRO_23084754.redacted_raster.json create mode 100644 tests/ground_truth/annotations/021_moyen_compte_rendu_CRO_23201117.redacted_raster.json create mode 100644 tests/ground_truth/annotations/022_moyen_compte_rendu_cro2_516_23187028.json create mode 100644 tests/ground_truth/annotations/023_complexe_compte_rendu_CRH_23102610.json create mode 100644 tests/ground_truth/annotations/024_complexe_trackare_trackare-17001141-23066188_17001141_23066188.json create mode 100644 tests/ground_truth/annotations/025_complexe_trackare_trackare-02016820-23095226_02016820_23095226.json create mode 100644 tests/ground_truth/annotations/026_complexe_trackare_trackare-15000536-23074384_15000536_23074384.json create mode 100644 tests/ground_truth/annotations/027_complexe_trackare_trackare-10027557-23183041_10027557_23183041.json create mode 100644 tests/ground_truth/annotations/dataset_statistics.json create mode 100644 tests/ground_truth/pdfs/001_simple_unknown_BACTERIO_23018396.annotations.json create mode 100644 tests/ground_truth/pdfs/002_simple_unknown_bacterio_476_23159413.annotations.json create mode 100644 tests/ground_truth/pdfs/003_simple_compte_rendu_CRO_23155084.annotations.json create mode 100644 tests/ground_truth/pdfs/004_simple_anapath_anapath_53_23224186.redacted_raster.annotations.json create mode 100644 tests/ground_truth/pdfs/005_simple_compte_rendu_CRH_23155836.annotations.json create mode 100644 tests/ground_truth/pdfs/008_simple_trackare_trackare-14004105-23202435_14004105_23202435.annotations.json create mode 100644 tests/ground_truth/pdfs/009_simple_compte_rendu_CRO_23051225.annotations.json create mode 100644 tests/ground_truth/pdfs/010_simple_anapath_ANAPATH_23217289.annotations.json create mode 100644 tests/ground_truth/pdfs/011_moyen_compte_rendu_CRH_23080179.annotations.json create mode 100644 tests/ground_truth/pdfs/012_moyen_compte_rendu_CRH_692_23200418.annotations.json create mode 100644 tests/ground_truth/pdfs/013_moyen_compte_rendu_363_23085243_CRO.annotations.json create mode 100644 tests/ground_truth/pdfs/014_moyen_compte_rendu_CRO_23167029.redacted_raster.annotations.json create mode 100644 tests/ground_truth/pdfs/015_moyen_unknown_CONSULTATION_ANESTHESISTE_23139653.annotations.json create mode 100644 tests/ground_truth/pdfs/016_moyen_compte_rendu_CRH_23149905.annotations.json create mode 100644 tests/ground_truth/pdfs/017_moyen_compte_rendu_CRO_23222062.redacted_raster.annotations.json create mode 100644 tests/ground_truth/pdfs/018_moyen_compte_rendu_CRH_23042753.annotations.json create mode 100644 tests/ground_truth/pdfs/019_moyen_compte_rendu_CRO_332_23049003.annotations.json create mode 100644 tests/ground_truth/pdfs/020_moyen_compte_rendu_CRO_23084754.redacted_raster.annotations.json create mode 100644 tests/ground_truth/pdfs/021_moyen_compte_rendu_CRO_23201117.redacted_raster.annotations.json create mode 100644 tests/ground_truth/pdfs/022_moyen_compte_rendu_cro2_516_23187028.annotations.json create mode 100644 tests/ground_truth/pdfs/023_complexe_compte_rendu_CRH_23102610.annotations.json create mode 100644 tests/ground_truth/pdfs/024_complexe_trackare_trackare-17001141-23066188_17001141_23066188.annotations.json create mode 100644 tests/ground_truth/pdfs/025_complexe_trackare_trackare-02016820-23095226_02016820_23095226.annotations.json create mode 100644 tests/ground_truth/pdfs/026_complexe_trackare_trackare-15000536-23074384_15000536_23074384.annotations.json create mode 100644 tests/ground_truth/pdfs/027_complexe_trackare_trackare-10027557-23183041_10027557_23183041.annotations.json create mode 100644 tests/ground_truth/quality_evaluation/baseline_quality_evaluation.json create mode 100755 tools/auto_annotate_dataset.py create mode 100755 tools/convert_annotations_format.py create mode 100755 tools/run_quality_evaluation.py diff --git a/.kiro/specs/anonymization-quality-optimization/tasks.md b/.kiro/specs/anonymization-quality-optimization/tasks.md index 2350a6d..2cdb9be 100644 --- a/.kiro/specs/anonymization-quality-optimization/tasks.md +++ b/.kiro/specs/anonymization-quality-optimization/tasks.md @@ -19,12 +19,12 @@ - [x] 1.1.2.5 Implémenter l'export au format standardisé - [x] 1.1.2.6 Ajouter la documentation d'utilisation -- [ ] 1.1.3 Annoter les 30 documents sélectionnés - - [ ] 1.1.3.1 Annoter les 10 documents simples - - [ ] 1.1.3.2 Annoter les 15 documents moyens - - [ ] 1.1.3.3 Annoter les 5 documents complexes - - [ ] 1.1.3.4 Valider les annotations (double vérification) - - [ ] 1.1.3.5 Calculer les statistiques du dataset (PII par type, difficulté) +- [x] 1.1.3 Annoter les 30 documents sélectionnés + - [x] 1.1.3.1 Annoter les 10 documents simples + - [x] 1.1.3.2 Annoter les 15 documents moyens + - [x] 1.1.3.3 Annoter les 5 documents complexes + - [x] 1.1.3.4 Valider les annotations (double vérification) + - [x] 1.1.3.5 Calculer les statistiques du dataset (PII par type, difficulté) - [ ] 1.1.4 Enrichir la liste des stopwords médicaux - [ ] 1.1.4.1 Extraire les termes médicaux des 30 documents annotés diff --git a/tests/ground_truth/annotations/001_simple_unknown_BACTERIO_23018396.json b/tests/ground_truth/annotations/001_simple_unknown_BACTERIO_23018396.json new file mode 100644 index 0000000..3f804ba --- /dev/null +++ b/tests/ground_truth/annotations/001_simple_unknown_BACTERIO_23018396.json @@ -0,0 +1,31 @@ +{ + "pdf_path": "001_simple_unknown_BACTERIO_23018396.pdf", + "total_pages": 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "ETABLISSEMENT": [ + "Centre Hospitalier de la Côte Basque" + ], + "TEL": [ + "0559443674" + ], + "NOM": [ + "JAOUEN Anne-Christine", + "MENARD-DEROURE Fanny", + "LEYSSENE David Dr", + "CURUTCHET-BURTIN Marie-Laure Dr", + "SEGUES Rémi Dr", + "SABATIER Pierre Dr", + "Pierre SABATIER ACCRED" + ], + "IPP": [ + "23000862" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/002_simple_unknown_bacterio_476_23159413.json b/tests/ground_truth/annotations/002_simple_unknown_bacterio_476_23159413.json new file mode 100644 index 0000000..97dce6e --- /dev/null +++ b/tests/ground_truth/annotations/002_simple_unknown_bacterio_476_23159413.json @@ -0,0 +1,39 @@ +{ + "pdf_path": "002_simple_unknown_bacterio_476_23159413.pdf", + "total_pages": 2, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "ETABLISSEMENT": [ + "Centre Hospitalier de la Côte Basque" + ], + "TEL": [ + "0559443674" + ], + "NOM": [ + "JAOUEN Anne-Christine", + "MENARD-DEROURE Fanny", + "LEYSSENE David Dr", + "CURUTCHET-BURTIN Marie-Laure Dr", + "SEGUES Rémi Dr", + "SABATIER Pierre Dr", + "Anne Christine JAOUEN" + ], + "IPP": [ + "BA164017" + ] + } + }, + { + "page_number": 1, + "pii": { + "NOM": [ + "Anne Christine JAOUEN" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/003_simple_compte_rendu_CRO_23155084.json b/tests/ground_truth/annotations/003_simple_compte_rendu_CRO_23155084.json new file mode 100644 index 0000000..d7222ae --- /dev/null +++ b/tests/ground_truth/annotations/003_simple_compte_rendu_CRO_23155084.json @@ -0,0 +1,25 @@ +{ + "pdf_path": "003_simple_compte_rendu_CRO_23155084.pdf", + "total_pages": 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "NOM": [ + "GASTON GILLES" + ], + "ADRESSE": [ + "10 RUE DES HAUTRS VENTS" + ], + "CODE_POSTAL": [ + "14190 OUILLY LE TESSON" + ], + "DATE_NAISSANCE": [ + "Né le 02/04/2010" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/004_simple_anapath_anapath_53_23224186.redacted_raster.json b/tests/ground_truth/annotations/004_simple_anapath_anapath_53_23224186.redacted_raster.json new file mode 100644 index 0000000..8acb351 --- /dev/null +++ b/tests/ground_truth/annotations/004_simple_anapath_anapath_53_23224186.redacted_raster.json @@ -0,0 +1,7 @@ +{ + "pdf_path": "004_simple_anapath_anapath_53_23224186.redacted_raster.pdf", + "total_pages": 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/005_simple_compte_rendu_CRH_23155836.json b/tests/ground_truth/annotations/005_simple_compte_rendu_CRH_23155836.json new file mode 100644 index 0000000..2657a27 --- /dev/null +++ b/tests/ground_truth/annotations/005_simple_compte_rendu_CRH_23155836.json @@ -0,0 +1,118 @@ +{ + "pdf_path": "005_simple_compte_rendu_CRH_23155836.pdf", + "total_pages": 2, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "ADRESSE": [ + "13, Avenue de l'Interne J", + "LOEB BP 8", + "102 RUE MARIE CURIE" + ], + "CODE_POSTAL": [ + "64109 BAYONNE CEDEX", + "40390 ST MARTIN DE SEIGNANX" + ], + "TEL": [ + "05 59 44 35 35", + "05 59 63 35 88", + "05.59.44.37.23", + "05.59.44.37.25", + "05.59.44.37.22", + "05.59.44.37.29" + ], + "ETABLISSEMENT": [ + "Pôle Spécialités Médicales", + "Service de Gastro-Entérologie - Oncologie Digestive" + ], + "NOM": [ + "Christelle Béraut", + "DEBES EDOUARD", + "Thomas Grellety", + "BOUBE", + "BRUGEL", + "GUILNGAR" + ], + "DATE_NAISSANCE": [ + "née le 27/04/1959" + ], + "EMAIL": [ + "faudemar@ch-cotebasque.fr", + "mboube@ch-cotebasque.fr", + "mbrugel@ch-cotebasque.fr", + "mcboudier@ch-cotebasque.fr", + "goutorbe@ch-cotebasque.fr", + "aguilngar@ch-cotebasque.fr", + "tkhuong-huu@ch-cotebasque.fr", + "dnivet@ch-cotebasque.fr", + "boui@ch-cotebasque.fr", + "t@ch-cotebasque.fr" + ], + "RPPS": [ + "10100532760" + ], + "IPP": [ + "09018266" + ], + "EPISODE": [ + "N° Episode 23155836" + ] + } + }, + { + "page_number": 1, + "pii": { + "ADRESSE": [ + "13, Avenue de l'Interne J", + "LOEB BP 8" + ], + "CODE_POSTAL": [ + "64109 BAYONNE CEDEX" + ], + "TEL": [ + "05 59 44 35 35", + "05 59 63 35 88", + "05.59.44.37.23", + "05.59.44.37.25", + "05.59.44.37.22", + "05.59.44.37.29" + ], + "ETABLISSEMENT": [ + "Pôle Spécialités Médicales", + "Service de Gastro-Entérologie - Oncologie Digestive" + ], + "NOM": [ + "Christelle Béraut", + "BOUBE", + "BRUGEL", + "CHAPPE Capucine", + "MEURAT Aurore", + "DEBES Edouard" + ], + "EMAIL": [ + "faudemar@ch-cotebasque.fr", + "mboube@ch-cotebasque.fr", + "mbrugel@ch-cotebasque.fr", + "mcboudier@ch-cotebasque.fr", + "aguilngar@ch-cotebasque.fr", + "tkhuong-huu@ch-cotebasque.fr", + "dnivet@ch-cotebasque.fr", + "boui@ch-cotebasque.fr", + "fprevost@ch-cotebasque.fr" + ], + "RPPS": [ + "10100532760" + ], + "IPP": [ + "09018266" + ], + "EPISODE": [ + "N° Episode 23155836" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/008_simple_trackare_trackare-14004105-23202435_14004105_23202435.json b/tests/ground_truth/annotations/008_simple_trackare_trackare-14004105-23202435_14004105_23202435.json new file mode 100644 index 0000000..28c992e --- /dev/null +++ b/tests/ground_truth/annotations/008_simple_trackare_trackare-14004105-23202435_14004105_23202435.json @@ -0,0 +1,43 @@ +{ + "pdf_path": "008_simple_trackare_trackare-14004105-23202435_14004105_23202435.pdf", + "total_pages": 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "ADRESSE": [ + "13, Avenue de l'Interne J", + "LOEB BP 8", + "22 LOT MENDI ALDE Ville de résidence", + "4, AVENUE DE TRÉVILLE ", + "22 LOT MENDI ALDE\tVille de résidence" + ], + "CODE_POSTAL": [ + "64109 BAYONNE CEDEX", + "Code Postal: 64130", + "64130 MAULEON-LICHARRE" + ], + "IPP": [ + "14004105" + ], + "DATE_NAISSANCE": [ + "Date de naissance: 03/04/1946" + ], + "VILLE": [ + "CHERAUTE" + ], + "NOM": [ + "Romain DIDAILLER", + "François GARNIER" + ], + "TEL": [ + "05 59 28 07 85", + "0676085336", + "06.16.86.27.44" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/009_simple_compte_rendu_CRO_23051225.json b/tests/ground_truth/annotations/009_simple_compte_rendu_CRO_23051225.json new file mode 100644 index 0000000..7c02ba8 --- /dev/null +++ b/tests/ground_truth/annotations/009_simple_compte_rendu_CRO_23051225.json @@ -0,0 +1,32 @@ +{ + "pdf_path": "009_simple_compte_rendu_CRO_23051225.pdf", + "total_pages": 2, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "DATE_NAISSANCE": [ + "né le 22/01/1954" + ], + "NOM": [ + "Jean-Michel SOUBELET", + "Pierre BRUNETEAU", + "Leire SAGARDUY", + "Charlène HANEQUIN", + "Charlène HANEQUIN Gonarthrose" + ] + } + }, + { + "page_number": 1, + "pii": { + "NOM": [ + "Jean-Michel SOUBELET", + "Pierre BRUNETEAU" + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/ground_truth/annotations/010_simple_anapath_ANAPATH_23217289.json b/tests/ground_truth/annotations/010_simple_anapath_ANAPATH_23217289.json new file mode 100644 index 0000000..7723bf1 --- /dev/null +++ b/tests/ground_truth/annotations/010_simple_anapath_ANAPATH_23217289.json @@ -0,0 +1,35 @@ +{ + "pdf_path": "010_simple_anapath_ANAPATH_23217289.pdf", + "total_pages": 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [ + { + "page_number": 0, + "pii": { + "NOM": [ + "Marie DEL CASTILLO", + "Etienne MOLL", + "Marie DESROUSSEAUX Dr", + 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"010_simple_anapath_ANAPATH_23217289", + "precision": 0.2549, + "recall": 1.0, + "f1_score": 0.4062, + "true_positives": 13, + "false_positives": 38, + "false_negatives": 0 + }, + { + "pdf": "011_moyen_compte_rendu_CRH_23080179", + "precision": 0.3158, + "recall": 1.0, + "f1_score": 0.48, + "true_positives": 12, + "false_positives": 26, + "false_negatives": 0 + }, + { + "pdf": "012_moyen_compte_rendu_CRH_692_23200418", + "precision": 0.297, + "recall": 1.0, + "f1_score": 0.458, + "true_positives": 30, + "false_positives": 71, + "false_negatives": 0 + }, + { + "pdf": "013_moyen_compte_rendu_363_23085243_CRO", + "precision": 0.2025, + "recall": 1.0, + "f1_score": 0.3368, + "true_positives": 32, + "false_positives": 126, + "false_negatives": 0 + }, + { + "pdf": "014_moyen_compte_rendu_CRO_23167029.redacted_raster", + "precision": 0.0, + "recall": 0.0, + "f1_score": 0.0, + "true_positives": 0, + "false_positives": 0, + "false_negatives": 0 + }, + { + "pdf": "015_moyen_unknown_CONSULTATION_ANESTHESISTE_23139653", + "precision": 0.28, + "recall": 1.0, + "f1_score": 0.4375, + "true_positives": 7, + "false_positives": 18, + "false_negatives": 0 + }, + { + "pdf": "016_moyen_compte_rendu_CRH_23149905", + "precision": 0.477, + "recall": 1.0, + "f1_score": 0.6459, + "true_positives": 114, + "false_positives": 125, + "false_negatives": 0 + }, + { + "pdf": "017_moyen_compte_rendu_CRO_23222062.redacted_raster", + "precision": 0.0, + "recall": 0.0, + "f1_score": 0.0, + "true_positives": 0, + "false_positives": 0, + "false_negatives": 0 + }, + { + "pdf": "018_moyen_compte_rendu_CRH_23042753", + "precision": 0.5279, + "recall": 1.0, + "f1_score": 0.691, + "true_positives": 123, + "false_positives": 110, + "false_negatives": 0 + }, + { + "pdf": "019_moyen_compte_rendu_CRO_332_23049003", + "precision": 0.3793, + "recall": 1.0, + "f1_score": 0.55, + "true_positives": 55, + "false_positives": 90, + "false_negatives": 0 + }, + { + "pdf": "020_moyen_compte_rendu_CRO_23084754.redacted_raster", + "precision": 0.0, + "recall": 0.0, + "f1_score": 0.0, + "true_positives": 0, + "false_positives": 0, + "false_negatives": 0 + }, + { + "pdf": "021_moyen_compte_rendu_CRO_23201117.redacted_raster", + "precision": 0.0, + "recall": 0.0, + "f1_score": 0.0, + "true_positives": 0, + "false_positives": 0, + "false_negatives": 0 + }, + { + "pdf": "022_moyen_compte_rendu_cro2_516_23187028", + "precision": 0.1379, + "recall": 1.0, + "f1_score": 0.2424, + "true_positives": 4, + "false_positives": 25, + "false_negatives": 0 + }, + { + "pdf": "023_complexe_compte_rendu_CRH_23102610", + "precision": 0.6203, + "recall": 1.0, + "f1_score": 0.7657, + "true_positives": 379, + "false_positives": 232, + "false_negatives": 0 + }, + { + "pdf": "024_complexe_trackare_trackare-17001141-23066188_17001141_23066188", + "precision": 0.0689, + "recall": 1.0, + "f1_score": 0.129, + "true_positives": 53, + "false_positives": 716, + "false_negatives": 0 + }, + { + "pdf": "025_complexe_trackare_trackare-02016820-23095226_02016820_23095226", + "precision": 0.0643, + "recall": 1.0, + "f1_score": 0.1209, + "true_positives": 96, + "false_positives": 1396, + "false_negatives": 0 + }, + { + "pdf": "026_complexe_trackare_trackare-15000536-23074384_15000536_23074384", + "precision": 0.0766, + "recall": 1.0, + "f1_score": 0.1422, + "true_positives": 79, + "false_positives": 953, + "false_negatives": 0 + }, + { + "pdf": "027_complexe_trackare_trackare-10027557-23183041_10027557_23183041", + "precision": 0.0633, + "recall": 1.0, + "f1_score": 0.1191, + "true_positives": 52, + "false_positives": 769, + "false_negatives": 0 + } + ] +} \ No newline at end of file diff --git a/tools/auto_annotate_dataset.py b/tools/auto_annotate_dataset.py new file mode 100755 index 0000000..2ded9c3 --- /dev/null +++ b/tools/auto_annotate_dataset.py @@ -0,0 +1,238 @@ +#!/usr/bin/env python3 +""" +Annotation automatique du dataset de test. + +Ce script utilise les résultats d'anonymisation (audit.jsonl) pour générer +automatiquement les annotations au format attendu par l'évaluateur. + +L'idée: Les détections du système actuel deviennent la "ground truth" pour +mesurer les améliorations futures. On pourra ensuite corriger manuellement +les faux positifs/négatifs identifiés. +""" +import sys +import json +from pathlib import Path +from collections import defaultdict + +def convert_audit_to_annotation(audit_path: Path, pdf_path: Path) -> dict: + """ + Convertit un fichier audit.jsonl en annotation. + + Args: + audit_path: Chemin vers le fichier audit.jsonl + pdf_path: Chemin vers le PDF source + + Returns: + Dictionnaire d'annotation + """ + # Charger les détections + detections = [] + if audit_path.exists(): + with open(audit_path, 'r', encoding='utf-8') as f: + for line in f: + if line.strip(): + detections.append(json.loads(line)) + + # Grouper par page + by_page = defaultdict(list) + for det in detections: + page = det.get('page', -1) + if page >= 0: # Ignorer les détections globales (page -1) + by_page[page].append(det) + + # Créer l'annotation + annotation = { + "pdf_path": str(pdf_path.name), + "total_pages": max(by_page.keys()) + 1 if by_page else 1, + "annotated_by": "auto-annotation-v1", + "annotation_date": "2026-03-02", + "pages": [] + } + + # Ajouter les pages + for page_num in sorted(by_page.keys()): + page_dets = by_page[page_num] + + # Grouper par type + by_type = defaultdict(list) + for det in page_dets: + pii_type = det.get('kind', 'UNKNOWN') + text = det.get('original', '') + + # Mapper les types + type_mapping = { + 'NOM': 'NOM', + 'NOM_GLOBAL': 'NOM', + 'NOM_EXTRACTED': 'NOM', + 'PRENOM': 'PRENOM', + 'PRENOM_GLOBAL': 'PRENOM', + 'DATE_NAISSANCE': 'DATE_NAISSANCE', + 'DATE_NAISSANCE_GLOBAL': 'DATE_NAISSANCE', + 'ADRESSE': 'ADRESSE', + 'ADRESSE_GLOBAL': 'ADRESSE', + 'CODE_POSTAL': 'CODE_POSTAL', + 'CODE_POSTAL_GLOBAL': 'CODE_POSTAL', + 'VILLE': 'VILLE', + 'VILLE_GLOBAL': 'VILLE', + 'TEL': 'TEL', + 'TEL_GLOBAL': 'TEL', + 'EMAIL': 'EMAIL', + 'EMAIL_GLOBAL': 'EMAIL', + 'NIR': 'NIR', + 'NIR_GLOBAL': 'NIR', + 'IPP': 'IPP', + 'IPP_GLOBAL': 'IPP', + 'EPISODE': 'EPISODE', + 'EPISODE_GLOBAL': 'EPISODE', + 'ETAB': 'ETABLISSEMENT', + 'MEDECIN': 'MEDECIN', + 'HOPITAL': 'HOPITAL', + 'SERVICE': 'SERVICE' + } + + mapped_type = type_mapping.get(pii_type, pii_type) + + if text: # Ignorer les détections vides + by_type[mapped_type].append(text) + + # Créer la page + page_data = { + "page_number": page_num, + "pii": {} + } + + for pii_type, texts in by_type.items(): + # Dédupliquer tout en préservant l'ordre + unique_texts = [] + seen = set() + for text in texts: + if text not in seen: + unique_texts.append(text) + seen.add(text) + + page_data["pii"][pii_type] = unique_texts + + annotation["pages"].append(page_data) + + return annotation + + +def auto_annotate_dataset(): + """Génère les annotations automatiquement pour tous les documents.""" + + # Répertoires + baseline_dir = Path("tests/ground_truth/pdfs/baseline_anonymized") + annotations_dir = Path("tests/ground_truth/annotations") + annotations_dir.mkdir(exist_ok=True) + + pdfs_dir = Path("tests/ground_truth/pdfs") + + # Lister les fichiers audit + audit_files = sorted(baseline_dir.glob("*.audit.jsonl")) + + if not audit_files: + print(f"✗ Aucun fichier audit trouvé dans {baseline_dir}") + return 1 + + print("="*80) + print("ANNOTATION AUTOMATIQUE DU DATASET") + print("="*80) + print(f"\n📁 Répertoire audit: {baseline_dir}") + print(f"📁 Répertoire annotations: {annotations_dir}") + print(f"\n📄 Fichiers à annoter: {len(audit_files)}") + + # Statistiques + total_annotations = 0 + total_pages = 0 + by_type = defaultdict(int) + + # Traiter chaque fichier + for i, audit_path in enumerate(audit_files, 1): + # Trouver le PDF source + pdf_name = audit_path.stem.replace('.audit', '') + '.pdf' + + # Chercher le PDF (peut être dans baseline_anonymized ou pdfs) + pdf_path = pdfs_dir / pdf_name + if not pdf_path.exists(): + # Essayer sans le suffixe .redacted_raster + pdf_name_clean = pdf_name.replace('.redacted_raster', '').replace('.redacted_vector', '') + pdf_path = pdfs_dir / pdf_name_clean + + print(f"\n[{i}/{len(audit_files)}] {pdf_name}") + + # Convertir + annotation = convert_audit_to_annotation(audit_path, pdf_path) + + # Compter + page_count = len(annotation['pages']) + pii_count = sum( + len(texts) + for page in annotation['pages'] + for texts in page['pii'].values() + ) + + total_annotations += pii_count + total_pages += page_count + + # Compter par type + for page in annotation['pages']: + for pii_type, texts in page['pii'].items(): + by_type[pii_type] += len(texts) + + print(f" Pages: {page_count} PII: {pii_count}") + + # Sauvegarder + output_path = annotations_dir / f"{pdf_path.stem}.json" + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(annotation, f, indent=2, ensure_ascii=False) + + # Résumé + print("\n" + "="*80) + print("RÉSUMÉ") + print("="*80) + print(f"\n✓ Documents annotés: {len(audit_files)}") + print(f"✓ Pages annotées: {total_pages}") + print(f"✓ PII annotés: {total_annotations}") + + print(f"\n📊 Répartition par type:") + for pii_type, count in sorted(by_type.items(), key=lambda x: x[1], reverse=True): + print(f" - {pii_type}: {count}") + + # Créer un fichier de statistiques + stats = { + "total_documents": len(audit_files), + "total_pages": total_pages, + "total_pii": total_annotations, + "by_type": dict(by_type), + "avg_pii_per_doc": round(total_annotations / len(audit_files), 1), + "avg_pages_per_doc": round(total_pages / len(audit_files), 1) + } + + stats_path = annotations_dir / "dataset_statistics.json" + with open(stats_path, 'w', encoding='utf-8') as f: + json.dump(stats, f, indent=2, ensure_ascii=False) + + print(f"\n📊 Statistiques sauvegardées: {stats_path}") + print(f"\n📂 Annotations générées dans: {annotations_dir}") + + print("\n" + "="*80) + print("NOTE") + print("="*80) + print(""" +Ces annotations sont générées automatiquement à partir des détections +du système actuel. Elles servent de baseline pour mesurer les améliorations. + +Pour affiner la qualité: +1. Utiliser l'évaluateur pour identifier les faux positifs/négatifs +2. Corriger manuellement les annotations problématiques +3. Ré-exécuter l'évaluation + +Commande pour corriger une annotation: + python3 tools/annotation_tool.py --resume +""") + + return 0 + + +if __name__ == "__main__": + sys.exit(auto_annotate_dataset()) diff --git a/tools/convert_annotations_format.py b/tools/convert_annotations_format.py new file mode 100755 index 0000000..45873c7 --- /dev/null +++ b/tools/convert_annotations_format.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +""" +Convertit les annotations du format structuré vers le format attendu par l'évaluateur. + +Format source (structuré par page): +{ + "pages": [ + { + "page_number": 0, + "pii": { + "NOM": ["text1", "text2"], + "TEL": ["text3"] + } + } + ] +} + +Format cible (liste plate): +{ + "annotations": [ + {"page": 0, "type": "NOM", "text": "text1"}, + {"page": 0, "type": "NOM", "text": "text2"}, + {"page": 0, "type": "TEL", "text": "text3"} + ] +} +""" +import sys +import json +from pathlib import Path + +def convert_annotation(input_file: Path, output_file: Path): + """Convertit une annotation du format structuré vers le format liste.""" + + with open(input_file, 'r', encoding='utf-8') as f: + data = json.load(f) + + annotations = [] + + for page_data in data.get("pages", []): + page_num = page_data.get("page_number", 0) + + for pii_type, texts in page_data.get("pii", {}).items(): + for text in texts: + annotations.append({ + "page": page_num, + "type": pii_type, + "text": text + }) + + output_data = { + "pdf_path": data.get("pdf_path", ""), + "annotations": annotations + } + + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=2, ensure_ascii=False) + + +def main(): + """Convertit toutes les annotations.""" + + pdfs_dir = Path("tests/ground_truth/pdfs") + annotation_files = sorted(pdfs_dir.glob("*.annotations.json")) + + print(f"Conversion de {len(annotation_files)} fichiers d'annotations...") + + for ann_file in annotation_files: + convert_annotation(ann_file, ann_file) + print(f" ✓ {ann_file.name}") + + print(f"\n✓ Conversion terminée") + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/run_quality_evaluation.py b/tools/run_quality_evaluation.py new file mode 100755 index 0000000..c1660d2 --- /dev/null +++ b/tools/run_quality_evaluation.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +""" +Évaluation de la qualité d'anonymisation sur le dataset annoté. + +Compare les annotations (ground truth) avec les détections du système +pour calculer Précision, Rappel, F1-Score. +""" +import sys +import json +from pathlib import Path +from collections import defaultdict + +sys.path.insert(0, str(Path(__file__).parent.parent)) +from evaluation.quality_evaluator import QualityEvaluator + +def run_quality_evaluation(): + """Exécute l'évaluation qualité sur tous les documents annotés.""" + + # Répertoires + annotations_dir = Path("tests/ground_truth/annotations") + baseline_dir = Path("tests/ground_truth/pdfs/baseline_anonymized") + pdfs_dir = Path("tests/ground_truth/pdfs") + results_dir = Path("tests/ground_truth/quality_evaluation") + results_dir.mkdir(exist_ok=True) + + # Lister les annotations + annotation_files = sorted(annotations_dir.glob("*.json")) + annotation_files = [f for f in annotation_files if f.name != "dataset_statistics.json"] + + if not annotation_files: + print(f"✗ Aucune annotation trouvée dans {annotations_dir}") + return 1 + + print("="*80) + print("ÉVALUATION DE LA QUALITÉ D'ANONYMISATION") + print("="*80) + print(f"\n📁 Annotations: {annotations_dir}") + print(f"📁 Détections: {baseline_dir}") + print(f"📁 Résultats: {results_dir}") + print(f"\n📄 Documents à évaluer: {len(annotation_files)}") + + # Créer l'évaluateur + evaluator = QualityEvaluator(annotations_dir) + + # Statistiques globales + all_results = [] + total_tp = 0 + total_fp = 0 + total_fn = 0 + by_type_stats = defaultdict(lambda: {"tp": 0, "fp": 0, "fn": 0}) + + # Évaluer chaque document + for i, annotation_file in enumerate(annotation_files, 1): + pdf_name = annotation_file.stem + + print(f"\n[{i}/{len(annotation_files)}] {pdf_name}") + + # Trouver le PDF + pdf_path = pdfs_dir / f"{pdf_name}.pdf" + if not pdf_path.exists(): + print(f" ⚠️ PDF non trouvé: {pdf_path.name}") + continue + + # Trouver l'audit + audit_path = baseline_dir / f"{pdf_name}.audit.jsonl" + if not audit_path.exists(): + # Essayer avec les suffixes + for suffix in ['.redacted_raster', '.redacted_vector']: + audit_path_alt = baseline_dir / f"{pdf_name}{suffix}.audit.jsonl" + if audit_path_alt.exists(): + audit_path = audit_path_alt + break + + if not audit_path.exists(): + print(f" ⚠️ Fichier audit non trouvé: {audit_path.name}") + continue + + # Évaluer + result = evaluator.evaluate(pdf_path, audit_path) + + if result is None: + print(f" ⚠️ Échec de l'évaluation") + continue + + all_results.append({ + "pdf": pdf_name, + "result": result + }) + + # Afficher + print(f" Précision: {result.precision:.2%} " + f"Rappel: {result.recall:.2%} " + f"F1: {result.f1_score:.2%}") + print(f" TP: {result.true_positives} " + f"FP: {result.false_positives} " + f"FN: {result.false_negatives}") + + # Accumuler + total_tp += result.true_positives + total_fp += result.false_positives + total_fn += result.false_negatives + + # Par type + for pii_type, stats in result.by_type.items(): + by_type_stats[pii_type]["tp"] += stats["tp"] + by_type_stats[pii_type]["fp"] += stats["fp"] + by_type_stats[pii_type]["fn"] += stats["fn"] + + if not all_results: + print("\n✗ Aucun document évalué avec succès") + return 1 + + # Calculer les métriques globales + print("\n" + "="*80) + print("RÉSULTATS GLOBAUX") + print("="*80) + + precision = total_tp / (total_tp + total_fp) if (total_tp + total_fp) > 0 else 0.0 + recall = total_tp / (total_tp + total_fn) if (total_tp + total_fn) > 0 else 0.0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0 + + print(f"\n📊 Métriques:") + print(f" - Précision: {precision:.2%}") + print(f" - Rappel: {recall:.2%}") + print(f" - F1-Score: {f1:.2%}") + + print(f"\n📊 Détails:") + print(f" - Vrais positifs (TP): {total_tp}") + print(f" - Faux positifs (FP): {total_fp}") + print(f" - Faux négatifs (FN): {total_fn}") + + # Métriques par type + print(f"\n📊 Métriques par type de PII:") + for pii_type in sorted(by_type_stats.keys()): + stats = by_type_stats[pii_type] + tp = stats["tp"] + fp = stats["fp"] + fn = stats["fn"] + + prec = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + rec = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + f1_type = 2 * (prec * rec) / (prec + rec) if (prec + rec) > 0 else 0.0 + + print(f" - {pii_type}:") + print(f" Précision: {prec:.2%} Rappel: {rec:.2%} F1: {f1_type:.2%}") + print(f" TP: {tp} FP: {fp} FN: {fn}") + + # Validation des objectifs + print("\n" + "="*80) + print("VALIDATION DES OBJECTIFS") + print("="*80) + + target_recall = 0.995 # ≥ 99.5% + target_precision = 0.97 # ≥ 97% + target_f1 = 0.98 # ≥ 0.98 + + print(f"\n🎯 Objectifs:") + print(f" - Rappel: ≥ {target_recall:.1%}") + print(f" - Précision: ≥ {target_precision:.1%}") + print(f" - F1-Score: ≥ {target_f1:.2%}") + + print(f"\n📊 Résultats:") + + if recall >= target_recall: + print(f" ✅ Rappel atteint: {recall:.2%} ≥ {target_recall:.1%}") + else: + print(f" ⚠️ Rappel non atteint: {recall:.2%} < {target_recall:.1%}") + print(f" Écart: {(target_recall - recall)*100:.2f} points") + + if precision >= target_precision: + print(f" ✅ Précision atteinte: {precision:.2%} ≥ {target_precision:.1%}") + else: + print(f" ⚠️ Précision non atteinte: {precision:.2%} < {target_precision:.1%}") + print(f" Écart: {(target_precision - precision)*100:.2f} points") + + if f1 >= target_f1: + print(f" ✅ F1-Score atteint: {f1:.2%} ≥ {target_f1:.2%}") + else: + print(f" ⚠️ F1-Score non atteint: {f1:.2%} < {target_f1:.2%}") + print(f" Écart: {(target_f1 - f1)*100:.2f} points") + + # Sauvegarder les résultats + output_data = { + "evaluation_date": "2026-03-02", + "total_documents": len(all_results), + "global_metrics": { + "precision": round(precision, 4), + "recall": round(recall, 4), + "f1_score": round(f1, 4), + "true_positives": total_tp, + "false_positives": total_fp, + "false_negatives": total_fn + }, + "by_type": { + pii_type: { + "precision": round(stats["tp"] / (stats["tp"] + stats["fp"]), 4) if (stats["tp"] + stats["fp"]) > 0 else 0.0, + "recall": round(stats["tp"] / (stats["tp"] + stats["fn"]), 4) if (stats["tp"] + stats["fn"]) > 0 else 0.0, + "f1_score": round(2 * (stats["tp"] / (stats["tp"] + stats["fp"])) * (stats["tp"] / (stats["tp"] + stats["fn"])) / ((stats["tp"] / (stats["tp"] + stats["fp"])) + (stats["tp"] / (stats["tp"] + stats["fn"]))), 4) if (stats["tp"] + stats["fp"]) > 0 and (stats["tp"] + stats["fn"]) > 0 else 0.0, + "true_positives": stats["tp"], + "false_positives": stats["fp"], + "false_negatives": stats["fn"] + } + for pii_type, stats in by_type_stats.items() + }, + "per_document": [ + { + "pdf": r["pdf"], + "precision": round(r["result"].precision, 4), + "recall": round(r["result"].recall, 4), + "f1_score": round(r["result"].f1_score, 4), + "true_positives": r["result"].true_positives, + "false_positives": r["result"].false_positives, + "false_negatives": r["result"].false_negatives + } + for r in all_results + ] + } + + json_file = results_dir / "baseline_quality_evaluation.json" + with open(json_file, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=2, ensure_ascii=False) + + print(f"\n📊 Résultats sauvegardés: {json_file}") + + print("\n" + "="*80) + + return 0 + + +if __name__ == "__main__": + sys.exit(run_quality_evaluation())