upload hubscripts/meddocan_hub.py to hub from bigbio repo

meddocan.py

@@ -0,0 +1,249 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+A dataset loading script for the MEDDOCAN corpus.
+The MEDDOCAN datset is a manually annotated collection of clinical case
+reports derived from the Spanish Clinical Case Corpus (SPACCC). It was designed
+for the Medical Document Anonymization Track, the first the first community
+challenge task specifically devoted to the anonymization of medical documents in Spanish
+"""
+
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['Spanish']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@inproceedings{marimon2019automatic,
+  title={Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results.},
+  author={Marimon, Montserrat and Gonzalez-Agirre, Aitor and Intxaurrondo, Ander and Rodriguez, Heidy and Martin, Jose Lopez and Villegas, Marta and Krallinger, Martin},
+  booktitle={IberLEF@ SEPLN},
+  pages={618--638},
+  year={2019}
+}
+"""
+
+_DATASETNAME = "meddocan"
+_DISPLAYNAME = "MEDDOCAN"
+
+_DESCRIPTION = """\
+MEDDOCAN: Medical Document Anonymization Track
+
+This dataset is designed for the MEDDOCAN task, sponsored by Plan de Impulso de las Tecnologías del Lenguaje.
+
+It is a manually classified collection of 1,000 clinical case reports derived from the \
+Spanish Clinical Case Corpus (SPACCC), enriched with PHI expressions.
+
+The annotation of the entire set of entity mentions was carried out by experts annotators\
+and it includes 29 entity types relevant for the annonymiation of medical documents.\
+22 of these annotation types are actually present in the corpus: TERRITORIO, FECHAS, \
+EDAD_SUJETO_ASISTENCIA, NOMBRE_SUJETO_ASISTENCIA, NOMBRE_PERSONAL_SANITARIO, \
+SEXO_SUJETO_ASISTENCIA, CALLE, PAIS, ID_SUJETO_ASISTENCIA, CORREO, ID_TITULACION_PERSONAL_SANITARIO,\
+ID_ASEGURAMIENTO, HOSPITAL, FAMILIARES_SUJETO_ASISTENCIA, INSTITUCION, ID_CONTACTO ASISTENCIAL,\
+NUMERO_TELEFONO, PROFESION, NUMERO_FAX, OTROS_SUJETO_ASISTENCIA, CENTRO_SALUD, ID_EMPLEO_PERSONAL_SANITARIO
+    
+For further information, please visit https://temu.bsc.es/meddocan/ or send an email to encargo-pln-life@bsc.es
+"""
+
+
+_HOMEPAGE = "https://temu.bsc.es/meddocan/"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    "meddocan": "https://zenodo.org/record/4279323/files/meddocan.zip?download=1",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MeddocanDataset(datasets.GeneratorBasedBuilder):
+    """Manually annotated collection of clinical case studies from Spanish medical publications."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="meddocan_source",
+            version=SOURCE_VERSION,
+            description="Meddocan source schema",
+            schema="source",
+            subset_id="meddocan",
+        ),
+        BigBioConfig(
+            name="meddocan_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="Meddocan BigBio schema",
+            schema="bigbio_kb",
+            subset_id="meddocan",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "meddocan_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    # "labels": [datasets.Value("string")],
+                    "text_bound_annotations": [  # T line in brat
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "type": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                        }
+                    ],
+                    "events": [  # E line in brat
+                        {
+                            "trigger": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arguments": datasets.Sequence(
+                                {
+                                    "role": datasets.Value("string"),
+                                    "ref_id": datasets.Value("string"),
+                                }
+                            ),
+                        }
+                    ],
+                    "relations": [  # R line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "head": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "tail": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                    "equivalences": [  # Equiv line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "ref_ids": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ],
+                    "attributes": [  # M or A lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                        }
+                    ],
+                    "normalizations": [  # N lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "resource_name": datasets.Value("string"),
+                            "cuid": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                        }
+                    ],
+                },
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """
+        Downloads/extracts the data to generate the train, validation and test splits.
+        Each split is created by instantiating a `datasets.SplitGenerator`, which will
+        call `this._generate_examples` with the keyword arguments in `gen_kwargs`.
+        """
+
+        data_dir = dl_manager.download_and_extract(_URLS["meddocan"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": Path(os.path.join(data_dir, "meddocan/train/brat")),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": Path(os.path.join(data_dir, "meddocan/test/brat")),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": Path(os.path.join(data_dir, "meddocan/dev/brat")),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """
+        This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        Method parameters are unpacked from `gen_kwargs` as given in `_split_generators`.
+        """
+
+        txt_files = sorted(list(filepath.glob("*txt")))
+        # tsv_files = sorted(list(filepaths[1].glob("*tsv")))
+
+        if self.config.schema == "source":
+            for guid, txt_file in enumerate(txt_files):
+                example = parsing.parse_brat_file(txt_file)
+
+                example["id"] = str(guid)
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            for guid, txt_file in enumerate(txt_files):
+                example = parsing.brat_parse_to_bigbio_kb(
+                    parsing.parse_brat_file(txt_file)
+                )
+                example["id"] = str(guid)
+                yield guid, example
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")