upload hubscripts/ctebmsp_hub.py to hub from bigbio repo

ctebmsp.py

@@ -0,0 +1,274 @@
+# 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.
+
+"""
+The Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) Corpus
+gathers 1200 texts about clinical trial studies for NER; this resource contains
+500 abstracts of journal articles about clinical trials and 700 announcements
+of trial protocols (292 173 tokens), with 46 699 annotated entities.
+
+Entities were annotated according to the Unified Medical Language System (UMLS)
+semantic groups: anatomy (ANAT), pharmacological and chemical substances (CHEM),
+pathologies (DISO), and lab tests, diagnostic or therapeutic procedures (PROC).
+"""
+
+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 = True
+_LOCAL = False
+_CITATION = """\
+@article{CampillosLlanos2021,
+  author    = {Leonardo Campillos-Llanos and
+               Ana Valverde-Mateos and
+               Adri{\'{a}}n Capllonch-Carri{\'{o}}n and
+               Antonio Moreno-Sandoval},
+  title     = {A clinical trials corpus annotated with {UMLS}
+               entities to enhance the access to evidence-based medicine},
+  journal   = {{BMC} Medical Informatics and Decision Making},
+  volume    = {21},
+  year      = {2021},
+  url       = {https://doi.org/10.1186/s12911-021-01395-z},
+  doi       = {10.1186/s12911-021-01395-z},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+
+_DATASETNAME = "ctebmsp"
+_DISPLAYNAME = "CT-EBM-SP"
+
+_ABSTRACTS_DESCRIPTION = """\
+The "abstracts" subset of the Clinical Trials for Evidence-Based Medicine in Spanish
+(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,
+published in journals with a Creative Commons license. Most were downloaded from
+the SciELO repository and free abstracts in PubMed.
+
+Abstracts were retrieved with the query:
+Clinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].
+
+(Information collected from 10.1186/s12911-021-01395-z)
+"""
+
+_EUDRACT_DESCRIPTION = """\
+The "abstracts" subset of the Clinical Trials for Evidence-Based Medicine in Spanish
+(CT-EBM-SP) corpus contains 500 abstracts of clinical trial studies in Spanish,
+published in journals with a Creative Commons license. Most were downloaded from
+the SciELO repository and free abstracts in PubMed.
+
+Abstracts were retrieved with the query:
+Clinical Trial[ptyp] AND “loattrfree full text”[sb] AND “spanish”[la].
+
+(Information collected from 10.1186/s12911-021-01395-z)
+"""
+
+_DESCRIPTION = {
+    "ctebmsp_abstracts": _ABSTRACTS_DESCRIPTION,
+    "ctebmsp_eudract": _EUDRACT_DESCRIPTION,
+}
+
+_HOMEPAGE = "http://www.lllf.uam.es/ESP/nlpmedterm_en.html"
+
+_LICENSE = 'Creative Commons Attribution Non Commercial 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "http://www.lllf.uam.es/ESP/nlpdata/wp2/CT-EBM-SP.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class CTEBMSpDataset(datasets.GeneratorBasedBuilder):
+    """A Spanish clinical trials corpus annotated with UMLS entities"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for study in ["abstracts", "eudract"]:
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"ctebmsp_{study}_source",
+                version=SOURCE_VERSION,
+                description=f"CT-EBM-SP {study.capitalize()} source schema",
+                schema="source",
+                subset_id=f"ctebmsp_{study}",
+            )
+        )
+
+        BUILDER_CONFIGS.append(
+            BigBioConfig(
+                name=f"ctebmsp_{study}_bigbio_kb",
+                version=BIGBIO_VERSION,
+                description=f"CT-EBM-SP {study.capitalize()} BigBio schema",
+                schema="bigbio_kb",
+                subset_id=f"ctebmsp_{study}",
+            ),
+        )
+
+    DEFAULT_CONFIG_NAME = "ctebmsp_abstracts_source"
+
+    # Entities from the Unified Medical Language System (UMLS) semantic groups
+
+    def _info(self) -> datasets.DatasetInfo:
+        """
+        Provide information about CT-EBM-SP
+        """
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
+                        {
+                            "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"
+                            ),  # refers to the text_bound_annotation of the trigger,
+                            "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"
+                            ),  # Name of the resource, e.g. "Wikipedia"
+                            "cuid": datasets.Value(
+                                "string"
+                            ),  # ID in the resource, e.g. 534366
+                            "text": datasets.Value(
+                                "string"
+                            ),  # Human readable description/name of the entity, e.g. "Barack Obama"
+                        }
+                    ],
+                },
+            )
+
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION[self.config.subset_id],
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+        studies_path = {
+            "ctebmsp_abstracts": "abstracts",
+            "ctebmsp_eudract": "eudract",
+        }
+
+        study_path = studies_path[self.config.subset_id]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"dir_files": data_dir / "train" / study_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"dir_files": data_dir / "test" / study_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"dir_files": data_dir / "dev" / study_path},
+            ),
+        ]
+
+    def _generate_examples(self, dir_files) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        txt_files = list(dir_files.glob("*txt"))
+
+        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}")