Create DEFT2021

DEFT2021

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+import os
+import json
+import random
+from pathlib import Path
+
+import datasets
+import numpy as np
+
+_CITATION = """\
+@inproceedings{grouin-etal-2021-classification,
+    title = "Classification de cas cliniques et {\'e}valuation automatique de r{\'e}ponses d{'}{\'e}tudiants : pr{\'e}sentation de la campagne {DEFT} 2021 (Clinical cases classification and automatic evaluation of student answers : Presentation of the {DEFT} 2021 Challenge)",
+    author = "Grouin, Cyril  and
+      Grabar, Natalia  and
+      Illouz, Gabriel",
+    booktitle = "Actes de la 28e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles. Atelier D{\'E}fi Fouille de Textes (DEFT)",
+    month = "6",
+    year = "2021",
+    address = "Lille, France",
+    publisher = "ATALA",
+    url = "https://aclanthology.org/2021.jeptalnrecital-deft.1",
+    pages = "1--13",
+    abstract = "Le d{\'e}fi fouille de textes (DEFT) est une campagne d{'}{\'e}valuation annuelle francophone. Nous pr{\'e}sentons les corpus et baselines {\'e}labor{\'e}es pour trois t{\^a}ches : (i) identifier le profil clinique de patients d{\'e}crits dans des cas cliniques, (ii) {\'e}valuer automatiquement les r{\'e}ponses d{'}{\'e}tudiants sur des questionnaires en ligne (Moodle) {\`a} partir de la correction de l{'}enseignant, et (iii) poursuivre une {\'e}valuation de r{\'e}ponses d{'}{\'e}tudiants {\`a} partir de r{\'e}ponses d{\'e}j{\`a} {\'e}valu{\'e}es par l{'}enseignant. Les r{\'e}sultats varient de 0,394 {\`a} 0,814 de F-mesure sur la premi{\`e}re t{\^a}che (7 {\'e}quipes), de 0,448 {\`a} 0,682 de pr{\'e}cision sur la deuxi{\`e}me (3 {\'e}quipes), et de 0,133 {\`a} 0,510 de pr{\'e}cision sur la derni{\`e}re (3 {\'e}quipes).",
+    language = "French",
+}
+"""
+
+_DESCRIPTION = """\
+ddd
+"""
+
+_HOMEPAGE = "ddd"
+
+_LICENSE = "unknown"
+
+_SPECIALITIES = ['immunitaire', 'endocriniennes', 'blessures', 'chimiques', 'etatsosy', 'nutritionnelles', 'infections', 'virales', 'parasitaires', 'tumeur', 'osteomusculaires', 'stomatognathique', 'digestif', 'respiratoire', 'ORL', 'nerveux', 'oeil', 'homme', 'femme', 'cardiovasculaires', 'hemopathies', 'genetique', 'peau']
+
+class DEFT2021(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "source"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="source", version="1.0.0", description="DEFT 2021 corpora"),
+    ]
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "document_id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "specialities": datasets.Sequence(
+                    datasets.features.ClassLabel(names=_SPECIALITIES),
+                ),
+                "specialities_one_hot": datasets.Sequence(
+                    datasets.Value("float"),
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        
+        else:
+            data_dir = self.config.data_dir
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, split):
+
+        all_res = {}
+
+        key = 0
+
+        if split == 'train' or split == 'validation':
+            split_eval = 'train'
+        else:
+            split_eval = 'test'
+
+        path_labels = Path(data_dir)  / 'evaluations' / f"ref-{split_eval}-deft2021.txt"
+
+        with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
+
+            doc_specialities_ = {}
+            with open(path_labels) as f_spec:
+                doc_specialities = [line.strip() for line in f_spec.readlines()]
+                for raw in doc_specialities:
+                    raw_split = raw.split('\t')
+                    if len(raw_split) == 3 and raw_split[0] in doc_specialities_:
+                        doc_specialities_[raw_split[0]].append(raw_split[1])
+                    elif len(raw_split) == 3 and raw_split[0] not in doc_specialities_:
+                        doc_specialities_[raw_split[0]] = [raw_split[1]]
+
+            ann_path = Path(data_dir) / "DEFT-cas-cliniques"
+
+            for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
+
+                ann_file = txt_file.with_suffix("").name.split('.')[0]+'.ann'
+
+                if ann_file in doc_specialities_:
+
+                    res = {}
+                    res['document_id'] = txt_file.with_suffix("").name
+                    with txt_file.open() as f:
+                        res["text"] = f.read()
+
+                    specialities = doc_specialities_[ann_file]
+
+                    # Empty one hot vector
+                    one_hot = [0.0 for i in _SPECIALITIES]
+
+                    # Fill up the one hot vector
+                    for s in specialities:
+                        one_hot[_SPECIALITIES.index(s)] = 1.0
+
+                    all_res[res['document_id']] = {
+                        "id": str(key),
+                        "document_id": res['document_id'],
+                        "text": res["text"],
+                        "specialities": specialities,
+                        "specialities_one_hot": one_hot,
+                    }
+
+                    key += 1
+
+            distribution = [line.strip() for line in f_dist.readlines()]
+            
+            random.seed(4)
+            train = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'train 2021']
+            random.shuffle(train)
+            random.shuffle(train)
+            random.shuffle(train)
+            train, validation = np.split(train, [int(len(train)*0.7096)])
+            
+            test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
+
+            if split == "train":
+                allowed_ids = list(train)
+            elif split == "test":
+                allowed_ids = list(test)
+            elif split == "validation":
+                allowed_ids = list(validation)
+
+            for r in all_res.values():
+                if r["document_id"]+'.txt' in allowed_ids:
+                    yield r["id"], r