clinical_trials.py

'''
Procesar así los datos en el terminal:

import clinical_trials

from clinical_trials import ClinicalTrials

train_json = ClinicalTrials._generate_examples('train.json','train.conll')

x = json.dumps([item for item in train_json])

outFile = open("train.json",'w',encoding="utf8")
print(x,file=outFile)
outFile.close()

'''


import datasets


logger = datasets.logging.get_logger(__name__)


_LICENSE = "Creative Commons Attribution 4.0 International"

_VERSION = "1.1.0"

_URL = "https://huggingface.co/datasets/lcampillos/CT-EBM-ES"
_TRAINING_FILE = "train.conll"
_DEV_FILE = "dev.conll"
_TEST_FILE = "test.conll"

class ClinicalTrialsConfig(datasets.BuilderConfig):
    """BuilderConfig for ClinicalTrials dataset."""

    def __init__(self, **kwargs):
        super(ClinicalTrialsConfig, self).__init__(**kwargs)


class ClinicalTrials(datasets.GeneratorBasedBuilder):
    """ClinicalTrials dataset."""

    BUILDER_CONFIGS = [
        ClinicalTrialsConfig(
            name="ClinicalTrials", 
            version=datasets.Version(_VERSION), 
            description="ClinicalTrials dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-ANAT",
                                "B-CHEM",
                                "B-DISO",
                                "B-PROC",
                                "I-ANAT",
                                "I-CHEM",
                                "I-DISO",
                                "I-PROC",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev":   f"{_URL}{_DEV_FILE}",
            "test":  f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            ner_tags = []
            for line in f:
                if line == "":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    splits = line.split("	")
                    tokens.append(splits[0])
                    ner_tags.append(splits[-1].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }