Delete loading script

CAS.py

@@ -1,395 +0,0 @@
-import os
-import random
-
-import datasets
-import numpy as np
-
-_CITATION = """\
-@inproceedings{grabar-etal-2018-cas,
-  title        = {{CAS}: {F}rench Corpus with Clinical Cases},
-  author       = {Grabar, Natalia  and Claveau, Vincent  and Dalloux, Cl{\'e}ment},
-  year         = 2018,
-  month        = oct,
-  booktitle    = {
-    Proceedings of the Ninth International Workshop on Health Text Mining and
-    Information Analysis
-  },
-  publisher    = {Association for Computational Linguistics},
-  address      = {Brussels, Belgium},
-  pages        = {122--128},
-  doi          = {10.18653/v1/W18-5614},
-  url          = {https://aclanthology.org/W18-5614},
-  abstract     = {
-    Textual corpora are extremely important for various NLP applications as
-    they provide information necessary for creating, setting and testing these
-    applications and the corresponding tools. They are also crucial for
-    designing reliable methods and reproducible results. Yet, in some areas,
-    such as the medical area, due to confidentiality or to ethical reasons, it
-    is complicated and even impossible to access textual data representative of
-    those produced in these areas. We propose the CAS corpus built with
-    clinical cases, such as they are reported in the published scientific
-    literature in French. We describe this corpus, currently containing over
-    397,000 word occurrences, and the existing linguistic and semantic
-    annotations.
-  }
-}
-"""
-
-_DESCRIPTION = """\
-We manually annotated two corpora from the biomedical field. The ESSAI corpus \
-contains clinical trial protocols in French. They were mainly obtained from the \
-National Cancer Institute The typical protocol consists of two parts: the \
-summary of the trial, which indicates the purpose of the trial and the methods \
-applied; and a detailed description of the trial with the inclusion and \
-exclusion criteria. The CAS corpus contains clinical cases published in \
-scientific literature and training material. They are published in different \
-journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
-African countries, tropical countries) and are related to various medical \
-specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
-gastro-enterology). The purpose of clinical cases is to describe clinical \
-situations of patients. Hence, their content is close to the content of clinical \
-narratives (description of diagnoses, treatments or procedures, evolution, \
-family history, expected audience, etc.). In clinical cases, the negation is \
-frequently used for describing the patient signs, symptoms, and diagnosis. \
-Speculation is present as well but less frequently.
-This version only contain the annotated CAS corpus
-"""
-
-_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
-
-_LICENSE = 'Data User Agreement'
-
-_URL = "data.zip"
-
-
-class CAS(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_CONFIG_NAME = "pos"
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="pos", version="1.0.0",
-                               description="The CAS corpora - POS Speculation task"),
-
-        datasets.BuilderConfig(name="cls", version="1.0.0",
-                               description="The CAS corpora - CLS Negation / Speculation task"),
-
-        datasets.BuilderConfig(name="ner_spec", version="1.0.0",
-                               description="The CAS corpora - NER Speculation task"),
-        datasets.BuilderConfig(name="ner_neg", version="1.0.0",
-                               description="The CAS corpora - NER Negation task"),
-    ]
-
-    def _info(self):
-
-        if self.config.name.find("pos") != -1:
-
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document_id": datasets.Value("string"),
-                    "tokens": [datasets.Value("string")],
-                    "lemmas": [datasets.Value("string")],
-                    "pos_tags": [datasets.features.ClassLabel(
-                        names=[
-                            'B-ABR', 'B-ADJ', 'B-ADV', 'B-DET:ART', 'B-DET:POS', 'B-INT',
-                            'B-KON', 'B-NAM', 'B-NOM', 'B-NUM',
-                            'B-PRO:DEM', 'B-PRO:IND', 'B-PRO:PER',
-                            'B-PRO:REL', 'B-PRP', 'B-PRP:det', 'B-PUN', 'B-PUN:cit',
-                            'B-SENT', 'B-SYM', 'B-VER:con', 'B-VER:cond', 'B-VER:futu',
-                            'B-VER:impf', 'B-VER:infi', 'B-VER:pper', 'B-VER:ppre',
-                            'B-VER:pres', 'B-VER:simp', 'B-VER:subi', 'B-VER:subp'
-                        ],
-                    )],
-                }
-            )
-
-        elif self.config.name.find("cls") != -1:
-
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document_id": datasets.Value("string"),
-                    "tokens": [datasets.Value("string")],
-                    "label": datasets.features.ClassLabel(
-                        names=['negation_speculation', 'negation', 'neutral', 'speculation'],
-                    ),
-                }
-            )
-
-        elif self.config.name.find("ner") != -1:
-
-            if self.config.name.find("_spec") != -1:
-                names = ['O', 'B_xcope_inc', 'I_xcope_inc']
-            elif self.config.name.find("_neg") != -1:
-                names = ['O', 'B_scope_neg', 'I_scope_neg']
-
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document_id": datasets.Value("string"),
-                    "tokens": [datasets.Value("string")],
-                    "lemmas": [datasets.Value("string")],
-                    "ner_tags": [datasets.features.ClassLabel(
-                        names=names,
-                    )],
-                }
-            )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=str(_LICENSE),
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_dir = dl_manager.download_and_extract(_URL).rstrip("/")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "datadir": data_dir,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "datadir": data_dir,
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "datadir": data_dir,
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, datadir, split):
-
-        all_res = []
-
-        key = 0
-
-        subset = self.config.name.split("_")[-1]
-
-        unique_id_doc = []
-
-        if self.config.name.find("ner") != -1:
-            docs = [f"CAS_{subset}.txt"]
-        else:
-            docs = ["CAS_neg.txt", "CAS_spec.txt"]
-
-        for file in docs:
-
-            filename = os.path.join(datadir, file)
-
-            if self.config.name.find("pos") != -1:
-
-                id_docs = []
-                id_words = []
-                words = []
-                lemmas = []
-                POS_tags = []
-
-                with open(filename) as f:
-
-                    for line in f.readlines():
-
-                        splitted = line.split("\t")
-
-                        if len(splitted) < 5:
-                            continue
-
-                        id_doc, id_word, word, lemma, tag = splitted[0:5]
-                        if len(splitted) >= 8:
-                            tag = splitted[6]
-
-                        if tag == "@card@":
-                            print(splitted)
-
-                        if word == "@card@":
-                            print(splitted)
-
-                        if lemma == "000" and tag == "@card@":
-                            tag = "NUM"
-                            word = "100 000"
-                            lemma = "100 000"
-                        elif lemma == "45" and tag == "@card@":
-                            tag = "NUM"
-
-                        # if id_doc in id_docs:
-                        #     continue
-
-                        id_docs.append(id_doc)
-                        id_words.append(id_word)
-                        words.append(word)
-                        lemmas.append(lemma)
-                        POS_tags.append(f'B-{tag}')
-
-                dic = {
-                    "id_docs": np.array(list(map(int, id_docs))),
-                    "id_words": id_words,
-                    "words": words,
-                    "lemmas": lemmas,
-                    "POS_tags": POS_tags,
-                }
-
-                for doc_id in set(dic["id_docs"]):
-
-                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                    tokens = [dic["words"][id] for id in indexes]
-                    text_lemmas = [dic["lemmas"][id] for id in indexes]
-                    pos_tags = [dic["POS_tags"][id] for id in indexes]
-
-                    if doc_id not in unique_id_doc:
-
-                        all_res.append({
-                            "id": str(doc_id),
-                            "document_id": doc_id,
-                            "tokens": tokens,
-                            "lemmas": text_lemmas,
-                            "pos_tags": pos_tags,
-                        })
-                        unique_id_doc.append(doc_id)
-
-                        # key += 1
-
-            elif self.config.name.find("ner") != -1:
-
-                id_docs = []
-                id_words = []
-                words = []
-                lemmas = []
-                ner_tags = []
-
-                with open(filename) as f:
-
-                    for line in f.readlines():
-
-                        if len(line.split("\t")) < 5:
-                            continue
-
-                        id_doc, id_word, word, lemma, _ = line.split("\t")[0:5]
-                        tag = line.replace("\n", "").split("\t")[-1]
-
-                        if tag == "***" or tag == "_":
-                            tag = "O"
-                        elif tag == "I_xcope_inc_":
-                            tag = "I_xcope_inc"
-                        # elif tag == "v":
-                        #     tag = "I_scope_spec"
-                        # elif tag == "z":
-                        #     tag = "O"
-
-                        id_docs.append(id_doc)
-                        id_words.append(id_word)
-                        words.append(word)
-                        lemmas.append(lemma)
-                        ner_tags.append(tag)
-
-                dic = {
-                    "id_docs": np.array(list(map(int, id_docs))),
-                    "id_words": id_words,
-                    "words": words,
-                    "lemmas": lemmas,
-                    "ner_tags": ner_tags,
-                }
-
-                for doc_id in set(dic["id_docs"]):
-
-                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
-                    tokens = [dic["words"][id] for id in indexes]
-                    text_lemmas = [dic["lemmas"][id] for id in indexes]
-                    ner_tags = [dic["ner_tags"][id] for id in indexes]
-
-                    all_res.append({
-                        "id": key,
-                        "document_id": doc_id,
-                        "tokens": tokens,
-                        "lemmas": text_lemmas,
-                        "ner_tags": ner_tags,
-                    })
-
-                    key += 1
-
-            elif self.config.name.find("cls") != -1:
-
-                f_in = open(filename, "r")
-                conll = [
-                    [b.split("\t") for b in a.split("\n")]
-                    for a in f_in.read().split("\n\n")
-                ]
-                f_in.close()
-
-                classe = "negation" if filename.find("_neg") != -1 else "speculation"
-
-                for document in conll:
-
-                    if document == [""]:
-                        continue
-
-                    identifier = document[0][0]
-
-                    unique = list(set([w[-1] for w in document]))
-                    tokens = [sent[2] for sent in document if len(sent) > 1]
-
-                    if "***" in unique:
-                        l = "neutral"
-                    elif "_" in unique:
-                        l = classe
-
-                    if identifier in unique_id_doc and l == 'neutral':
-                        continue
-
-                    elif identifier in unique_id_doc and l != 'neutral':
-
-                        index_l = unique_id_doc.index(identifier)
-
-                        if all_res[index_l]["label"] != "neutral":
-                            l = "negation_speculation"
-
-                        all_res[index_l] = {
-                            "id": str(identifier),
-                            "document_id": identifier,
-                            "tokens": tokens,
-                            "label": l,
-                        }
-
-                    else:
-
-                        all_res.append({
-                            "id": str(identifier),
-                            "document_id": identifier,
-                            "tokens": tokens,
-                            "label": l,
-                        })
-
-                        unique_id_doc.append(identifier)
-
-        ids = [r["id"] for r in all_res]
-
-        random.seed(4)
-        random.shuffle(ids)
-        random.shuffle(ids)
-        random.shuffle(ids)
-
-        train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)])
-
-        if split == "train":
-            allowed_ids = list(train)
-        elif split == "validation":
-            allowed_ids = list(validation)
-        elif split == "test":
-            allowed_ids = list(test)
-
-        for r in all_res:
-            if r["id"] in allowed_ids:
-                yield r["id"], r