upload hubscripts/ncbi_disease_hub.py to hub from bigbio repo

ncbi_disease.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.
+
+"""
+The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
+resource for the biomedical natural language processing community.
+"""
+
+import os
+from typing import Dict, Iterator, List, Tuple
+
+import datasets
+from bioc import pubtator
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{Dogan2014NCBIDC,
+    title        = {NCBI disease corpus: A resource for disease name recognition and concept normalization},
+    author       = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu},
+    year         = 2014,
+    journal      = {Journal of biomedical informatics},
+    volume       = 47,
+    pages        = {1--10}
+}
+"""
+
+_DATASETNAME = "ncbi_disease"
+_DISPLAYNAME = "NCBI Disease"
+
+_DESCRIPTION = """\
+The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
+resource for the biomedical natural language processing community.
+"""
+
+_HOMEPAGE = "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/"
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+_URLS = {
+    _DATASETNAME: {
+        datasets.Split.TRAIN: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItrainset_corpus.zip",
+        datasets.Split.TEST: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBItestset_corpus.zip",
+        datasets.Split.VALIDATION: "https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/NCBIdevelopset_corpus.zip",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class NCBIDiseaseDataset(datasets.GeneratorBasedBuilder):
+    """NCBI Disease"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="ncbi_disease_source",
+            version=SOURCE_VERSION,
+            description="NCBI Disease source schema",
+            schema="source",
+            subset_id="ncbi_disease",
+        ),
+        BigBioConfig(
+            name="ncbi_disease_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="NCBI Disease BigBio schema",
+            schema="bigbio_kb",
+            subset_id="ncbi_disease",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ncbi_disease_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pmid": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "mentions": [
+                        {
+                            "concept_id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offsets": datasets.Sequence(datasets.Value("int32")),
+                        }
+                    ],
+                }
+            )
+
+        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]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        train_filename = "NCBItrainset_corpus.txt"
+        test_filename = "NCBItestset_corpus.txt"
+        dev_filename = "NCBIdevelopset_corpus.txt"
+
+        train_filepath = os.path.join(data_dir[datasets.Split.TRAIN], train_filename)
+        test_filepath = os.path.join(data_dir[datasets.Split.TEST], test_filename)
+        dev_filepath = os.path.join(data_dir[datasets.Split.VALIDATION], dev_filename)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_filepath,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_filepath,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": dev_filepath,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath: str, split: str
+    ) -> Iterator[Tuple[str, Dict]]:
+        if self.config.schema == "source":
+            for i, source_example in enumerate(self._pubtator_to_source(filepath)):
+                # Some examples are duplicated in NCBI Disease. We have to make them unique to
+                # avoid and error from datasets.
+                yield str(i) + "_" + source_example["pmid"], source_example
+
+        elif self.config.schema == "bigbio_kb":
+            seen = []
+            for kb_example in self._pubtator_to_bigbio_kb(filepath):
+                # Some examples are duplicated in NCBI Disease. Avoid yielding more than once.
+                if kb_example["id"] in seen:
+                    continue
+                yield kb_example["id"], kb_example
+                seen.append(kb_example["id"])
+
+    @staticmethod
+    def _pubtator_to_source(filepath: Dict) -> Iterator[Dict]:
+        with open(filepath, "r") as f:
+            for doc in pubtator.iterparse(f):
+                source_example = {
+                    "pmid": doc.pmid,
+                    "title": doc.title,
+                    "abstract": doc.abstract,
+                    "mentions": [
+                        {
+                            "concept_id": mention.id,
+                            "type": mention.type,
+                            "text": mention.text,
+                            "offsets": [mention.start, mention.end],
+                        }
+                        for mention in doc.annotations
+                    ],
+                }
+                yield source_example
+
+    @staticmethod
+    def _pubtator_to_bigbio_kb(filepath: Dict) -> Iterator[Dict]:
+        with open(filepath, "r") as f:
+            unified_example = {}
+            for doc in pubtator.iterparse(f):
+                unified_example["id"] = doc.pmid
+                unified_example["document_id"] = doc.pmid
+
+                unified_example["passages"] = [
+                    {
+                        "id": doc.pmid + "_title",
+                        "type": "title",
+                        "text": [doc.title],
+                        "offsets": [[0, len(doc.title)]],
+                    },
+                    {
+                        "id": doc.pmid + "_abstract",
+                        "type": "abstract",
+                        "text": [doc.abstract],
+                        "offsets": [
+                            [
+                                # +1 assumes the title and abstract will be joined by a space.
+                                len(doc.title) + 1,
+                                len(doc.title) + 1 + len(doc.abstract),
+                            ]
+                        ],
+                    },
+                ]
+
+                unified_entities = []
+                for i, entity in enumerate(doc.annotations):
+                    # We need a unique identifier for this entity, so build it from the document id and entity id
+                    unified_entity_id = "_".join([doc.pmid, entity.id, str(i)])
+                    # The user can provide a callable that returns the database name.
+                    db_name = "omim" if "OMIM" in entity.id else "mesh"
+                    unified_entities.append(
+                        {
+                            "id": unified_entity_id,
+                            "type": entity.type,
+                            "text": [entity.text],
+                            "offsets": [[entity.start, entity.end]],
+                            "normalized": [{"db_name": db_name, "db_id": entity.id}],
+                        }
+                    )
+
+                unified_example["entities"] = unified_entities
+                unified_example["relations"] = []
+                unified_example["events"] = []
+                unified_example["coreferences"] = []
+
+                yield unified_example