upload hubscripts/nlm_gene_hub.py to hub from bigbio repo

nlm_gene.py

@@ -0,0 +1,265 @@
+# 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.
+import collections
+import itertools
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+from bioc import biocxml
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{islamaj2021nlm,
+  title        = {
+    NLM-Gene, a richly annotated gold standard dataset for gene entities that
+    addresses ambiguity and multi-species gene recognition
+  },
+  author       = {
+    Islamaj, Rezarta and Wei, Chih-Hsuan and Cissel, David and Miliaras,
+    Nicholas and Printseva, Olga and Rodionov, Oleg and Sekiya, Keiko and Ward,
+    Janice and Lu, Zhiyong
+  },
+  year         = 2021,
+  journal      = {Journal of Biomedical Informatics},
+  publisher    = {Elsevier},
+  volume       = 118,
+  pages        = 103779
+}
+"""
+
+_DATASETNAME = "nlm_gene"
+_DISPLAYNAME = "NLM-Gene"
+
+_DESCRIPTION = """\
+NLM-Gene consists of 550 PubMed articles, from 156 journals, and contains more \
+than 15 thousand unique gene names, corresponding to more than five thousand \
+gene identifiers (NCBI Gene taxonomy). This corpus contains gene annotation data \
+from 28 organisms. The annotated articles contain on average 29 gene names, and \
+10 gene identifiers per article. These characteristics demonstrate that this \
+article set is an important benchmark dataset to test the accuracy of gene \
+recognition algorithms both on multi-species and ambiguous data. The NLM-Gene \
+corpus will be invaluable for advancing text-mining techniques for gene \
+identification tasks in biomedical text.
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/5089049"
+
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+
+_URLS = {
+    "source": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip",
+    "bigbio_kb": "https://zenodo.org/record/5089049/files/NLM-Gene-Corpus.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class NLMGeneDataset(datasets.GeneratorBasedBuilder):
+    """NLM-Gene Dataset for gene entities"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="nlm_gene_source",
+            version=SOURCE_VERSION,
+            description="NlM Gene source schema",
+            schema="source",
+            subset_id="nlm_gene",
+        ),
+        BigBioConfig(
+            name="nlm_gene_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="NlM Gene BigBio schema",
+            schema="bigbio_kb",
+            subset_id="nlm_gene",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "nlm_gene_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            if self.config.schema == "source":
+                # this is a variation on the BioC format
+                features = datasets.Features(
+                    {
+                        "passages": [
+                            {
+                                "document_id": datasets.Value("string"),
+                                "type": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "entities": [
+                                    {
+                                        "id": datasets.Value("string"),
+                                        "offsets": [[datasets.Value("int32")]],
+                                        "text": [datasets.Value("string")],
+                                        "type": datasets.Value("string"),
+                                        "normalized": [
+                                            {
+                                                "db_name": datasets.Value("string"),
+                                                "db_id": 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]:
+        """Returns SplitGenerators."""
+        urls = _URLS[self.config.schema]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir / "Corpus",
+                    "file_name": "Pmidlist.Train.txt",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir / "Corpus",
+                    "file_name": "Pmidlist.Test.txt",
+                    "split": "test",
+                },
+            ),
+        ]
+
+    @staticmethod
+    def _get_bioc_entity(
+        span, db_id_key="NCBI Gene identifier", splitters=",;|-"
+    ) -> dict:
+        """Parse BioC entity annotation."""
+        offsets, texts = get_texts_and_offsets_from_bioc_ann(span)
+        db_ids = span.infons.get(db_id_key, "-1")
+        # Find connector between db_ids for the normalization, if not found, use default
+        connector = "|"
+        for splitter in list(splitters):
+            if splitter in db_ids:
+                connector = splitter
+        normalized = [
+            {"db_name": db_id_key, "db_id": db_id} for db_id in db_ids.split(connector)
+        ]
+
+        return {
+            "id": span.id,
+            "offsets": offsets,
+            "text": texts,
+            "type": span.infons["type"],
+            "normalized": normalized,
+        }
+
+    def _generate_examples(
+        self, filepath: Path, file_name: str, split: str
+    ) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            with open(filepath / file_name, encoding="utf-8") as f:
+                contents = f.readlines()
+            for uid, content in enumerate(contents):
+                file_id = content.replace("\n", "")
+                file_path = filepath / "FINAL" / f"{file_id}.BioC.XML"
+                reader = biocxml.BioCXMLDocumentReader(file_path.as_posix())
+                for xdoc in reader:
+                    yield uid, {
+                        "passages": [
+                            {
+                                "document_id": xdoc.id,
+                                "type": passage.infons["type"],
+                                "text": passage.text,
+                                "entities": [
+                                    self._get_bioc_entity(span)
+                                    for span in passage.annotations
+                                ],
+                            }
+                            for passage in xdoc.passages
+                        ]
+                    }
+        elif self.config.schema == "bigbio_kb":
+            with open(filepath / file_name, encoding="utf-8") as f:
+                contents = f.readlines()
+            uid = 0  # global unique id
+            for i, content in enumerate(contents):
+                file_id = content.replace("\n", "")
+                file_path = filepath / "FINAL" / f"{file_id}.BioC.XML"
+                reader = biocxml.BioCXMLDocumentReader(file_path.as_posix())
+                for xdoc in reader:
+                    data = {
+                        "id": uid,
+                        "document_id": xdoc.id,
+                        "passages": [],
+                        "entities": [],
+                        "relations": [],
+                        "events": [],
+                        "coreferences": [],
+                    }
+                    uid += 1
+
+                    char_start = 0
+                    # passages must not overlap and spans must cover the entire document
+                    for passage in xdoc.passages:
+                        offsets = [[char_start, char_start + len(passage.text)]]
+                        char_start = char_start + len(passage.text) + 1
+                        data["passages"].append(
+                            {
+                                "id": uid,
+                                "type": passage.infons["type"],
+                                "text": [passage.text],
+                                "offsets": offsets,
+                            }
+                        )
+                        uid += 1
+                    # entities
+                    for passage in xdoc.passages:
+                        for span in passage.annotations:
+                            ent = self._get_bioc_entity(
+                                span, db_id_key="NCBI Gene identifier"
+                            )
+                            ent["id"] = uid  # override BioC default id
+                            data["entities"].append(ent)
+                            uid += 1
+
+                    yield i, data