Delete loading script

E3C.py

@@ -1,296 +0,0 @@
-# pip install bs4 syntok
-
-import os
-import random
-
-import datasets
-
-import numpy as np
-from bs4 import BeautifulSoup, ResultSet
-from syntok.tokenizer import Tokenizer
-
-tokenizer = Tokenizer()
-
-_CITATION = """\
-@report{Magnini2021, \
-    author = {Bernardo Magnini and Begoña Altuna and Alberto Lavelli and Manuela Speranza \
-    and Roberto Zanoli and Fondazione Bruno Kessler}, \
-    keywords = {Clinical data,clinical enti-ties,corpus,multilingual,temporal information}, \
-    title = {The E3C Project: \
-    European Clinical Case Corpus El proyecto E3C: European Clinical Case Corpus}, \
-    url = {https://uts.nlm.nih.gov/uts/umls/home}, \
-    year = {2021}, \
-}
-"""
-
-_DESCRIPTION = """\
-E3C is a freely available multilingual corpus (English, French, Italian, Spanish, and Basque) \
-of semantically annotated clinical narratives to allow for the linguistic analysis, benchmarking, \
-and training of information extraction systems. It consists of two types of annotations: \
-(i) clinical entities (e.g., pathologies), (ii) temporal information and factuality (e.g., events). \
-Researchers can use the benchmark training and test splits of our corpus to develop and test \
-their own models.
-"""
-
-_URL = "https://github.com/hltfbk/E3C-Corpus/archive/refs/tags/v2.0.0.zip"
-
-_LANGUAGES = ["English","Spanish","Basque","French","Italian"]
-
-class E3C(datasets.GeneratorBasedBuilder):
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=f"{lang}_clinical", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
-    ]
-
-    BUILDER_CONFIGS += [
-        datasets.BuilderConfig(name=f"{lang}_temporal", version="1.0.0", description=f"The {lang} subset of the E3C corpus") for lang in _LANGUAGES
-    ]    
-
-    DEFAULT_CONFIG_NAME = "French_clinical"
-
-    def _info(self):
-        
-        if self.config.name == "default":
-            self.config.name = self.DEFAULT_CONFIG_NAME
-
-        if self.config.name.find("clinical") != -1:
-            names = ["O","B-CLINENTITY","I-CLINENTITY"]
-        elif self.config.name.find("temporal") != -1:
-            names = ["O", "B-EVENT", "B-ACTOR", "B-BODYPART", "B-TIMEX3", "B-RML", "I-EVENT", "I-ACTOR", "I-BODYPART", "I-TIMEX3", "I-RML"]
-
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=names,
-                    ),
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            citation=_CITATION,
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_dir = dl_manager.download_and_extract(_URL)
-
-        if self.config.name.find("clinical") != -1:
-
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer2"),
-                        "split": "validation",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_clinical",""), "layer1"),
-                        "split": "test",
-                    },
-                ),
-            ]
-
-        elif self.config.name.find("temporal") != -1:
-
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
-                        "split": "train",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
-                        "split": "validation",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "filepath": os.path.join(data_dir, "E3C-Corpus-2.0.0/data_annotation", self.config.name.replace("_temporal",""), "layer1"),
-                        "split": "test",
-                    },
-                ),
-            ]
-
-    @staticmethod
-    def get_annotations(entities: ResultSet, text: str) -> list:
-
-        return [[
-            int(entity.get("begin")),
-            int(entity.get("end")),
-            text[int(entity.get("begin")) : int(entity.get("end"))],
-        ] for entity in entities]
-
-    def get_clinical_annotations(self, entities: ResultSet, text: str) -> list:
-
-        return [[
-            int(entity.get("begin")),
-            int(entity.get("end")),
-            text[int(entity.get("begin")) : int(entity.get("end"))],
-            entity.get("entityID"),
-        ] for entity in entities]
-
-    def get_parsed_data(self, filepath: str):
-
-        for root, _, files in os.walk(filepath):
-
-            for file in files:
-
-                with open(f"{root}/{file}") as soup_file:
-
-                    soup = BeautifulSoup(soup_file, "xml")
-                    text = soup.find("cas:Sofa").get("sofaString")
-
-                    yield {
-                        "CLINENTITY": self.get_clinical_annotations(soup.find_all("custom:CLINENTITY"), text),
-                        "EVENT": self.get_annotations(soup.find_all("custom:EVENT"), text),
-                        "ACTOR": self.get_annotations(soup.find_all("custom:ACTOR"), text),
-                        "BODYPART": self.get_annotations(soup.find_all("custom:BODYPART"), text),
-                        "TIMEX3": self.get_annotations(soup.find_all("custom:TIMEX3"), text),
-                        "RML": self.get_annotations(soup.find_all("custom:RML"), text),
-                        "SENTENCE": self.get_annotations(soup.find_all("type4:Sentence"), text),
-                        "TOKENS": self.get_annotations(soup.find_all("type4:Token"), text),
-                    }
-
-    def _generate_examples(self, filepath, split):
-
-        all_res = []
-
-        key = 0
-
-        parsed_content = self.get_parsed_data(filepath)
-
-        for content in parsed_content:
-
-            for sentence in content["SENTENCE"]:
-
-                tokens = [(
-                    token.offset + sentence[0],
-                    token.offset + sentence[0] + len(token.value),
-                    token.value,
-                ) for token in list(tokenizer.tokenize(sentence[-1]))]
-
-                filtered_tokens = list(
-                    filter(
-                        lambda token: token[0] >= sentence[0] and token[1] <= sentence[1],
-                        tokens,
-                    )
-                )
-
-                tokens_offsets = [
-                    [token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens
-                ]
-
-                clinical_labels = ["O"] * len(filtered_tokens)
-                clinical_cuid = ["CUI_LESS"] * len(filtered_tokens)
-                temporal_information_labels = ["O"] * len(filtered_tokens)
-
-                for entity_type in ["CLINENTITY","EVENT","ACTOR","BODYPART","TIMEX3","RML"]:
-
-                    if len(content[entity_type]) != 0:
-
-                        for entities in list(content[entity_type]):
-
-                            annotated_tokens = [
-                                idx_token
-                                for idx_token, token in enumerate(filtered_tokens)
-                                if token[0] >= entities[0] and token[1] <= entities[1]
-                            ]
-
-                            for idx_token in annotated_tokens:
-
-                                if entity_type == "CLINENTITY":
-                                    if idx_token == annotated_tokens[0]:
-                                        clinical_labels[idx_token] = f"B-{entity_type}"
-                                    else:
-                                        clinical_labels[idx_token] = f"I-{entity_type}"
-                                    clinical_cuid[idx_token] = entities[-1]
-                                else:
-                                    if idx_token == annotated_tokens[0]:
-                                        temporal_information_labels[idx_token] = f"B-{entity_type}"
-                                    else:
-                                        temporal_information_labels[idx_token] = f"I-{entity_type}"
-
-                if self.config.name.find("clinical") != -1:
-                    _labels = clinical_labels        
-                elif self.config.name.find("temporal") != -1:
-                    _labels = temporal_information_labels
-
-                all_res.append({
-                    "id": key,
-                    "text": sentence[-1],
-                    "tokens": list(map(lambda token: token[2], filtered_tokens)),
-                    "ner_tags": _labels,
-                })
-
-                key += 1
-
-        if self.config.name.find("clinical") != -1:
-
-            if split != "test":
-
-                ids = [r["id"] for r in all_res]
-
-                random.seed(4)
-                random.shuffle(ids)
-                random.shuffle(ids)
-                random.shuffle(ids)
-                
-                train, validation = np.split(ids, [int(len(ids)*0.8738)])
-
-                if split == "train":
-                    allowed_ids = list(train)
-                elif split == "validation":
-                    allowed_ids = list(validation)
-
-                for r in all_res:
-                    if r["id"] in allowed_ids:
-                        yield r["id"], r
-            else:
-
-                for r in all_res:
-                    yield r["id"], r
-
-        elif self.config.name.find("temporal") != -1:
-
-            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