upload hubscripts/anat_em_hub.py to hub from bigbio repo

anat_em.py

@@ -0,0 +1,227 @@
+# 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 extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents
+(approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical
+entities. Each annotation is assigned one of 12 granularity-based types such as Cellular
+component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology
+(see https://bioportal.bioontology.org/ontologies/CARO).
+"""
+from pathlib import Path
+from typing import Dict, Iterator, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{pyysalo2014anatomical,
+  title={Anatomical entity mention recognition at literature scale},
+  author={Pyysalo, Sampo and Ananiadou, Sophia},
+  journal={Bioinformatics},
+  volume={30},
+  number={6},
+  pages={868--875},
+  year={2014},
+  publisher={Oxford University Press}
+}
+"""
+
+_DATASETNAME = "anat_em"
+_DISPLAYNAME = "AnatEM"
+
+
+_DESCRIPTION = """\
+The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 \
+documents (approx. 250,000 words) manually annotated to identify over 13,000 \
+mentions of anatomical entities. Each annotation is assigned one of 12 \
+granularity-based types such as Cellular component, Tissue and Organ, defined \
+with reference to the Common Anatomy Reference Ontology.
+"""
+
+_HOMEPAGE = "http://nactem.ac.uk/anatomytagger/#AnatEM"
+
+_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported'
+
+_URLS = {_DATASETNAME: "http://nactem.ac.uk/anatomytagger/AnatEM-1.0.2.tar.gz"}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.2"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class AnatEMDataset(datasets.GeneratorBasedBuilder):
+    """The extended Anatomical Entity Mention corpus (AnatEM)"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="anat_em_source",
+            version=SOURCE_VERSION,
+            description="AnatEM source schema",
+            schema="source",
+            subset_id="anat_em",
+        ),
+        BigBioConfig(
+            name="anat_em_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="AnatEM BigBio schema",
+            schema="bigbio_kb",
+            subset_id="anat_em",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "anat_em_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "document_type": datasets.Value("string"),  # Either PMC or PM
+                    "text": datasets.Value("string"),
+                    "text_type": datasets.Value(
+                        "string"
+                    ),  # Either abstract (for PM) or sec / caption (for PMC)
+                    "entities": [
+                        {
+                            "entity_id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(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):
+        urls = _URLS[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+
+        standoff_dir = data_dir / "AnatEM-1.0.2" / "standoff"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split_dir": standoff_dir / "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split_dir": standoff_dir / "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split_dir": standoff_dir / "devel"},
+            ),
+        ]
+
+    def _generate_examples(self, split_dir: Path) -> Iterator[Tuple[str, Dict]]:
+        if self.config.name == "anat_em_source":
+            for file in split_dir.iterdir():
+                # Ignore hidden files and annotation files - we just consider the brat text files
+                if file.name.startswith("._") or file.name.endswith(".ann"):
+                    continue
+
+                # Read brat annotations for the given text file and convert example to the source format
+                brat_example = parsing.parse_brat_file(file)
+                source_example = self._to_source_example(file, brat_example)
+
+                yield source_example["document_id"], source_example
+
+        elif self.config.name == "anat_em_bigbio_kb":
+            for file in split_dir.iterdir():
+                # Ignore hidden files and annotation files - we just consider the brat text files
+                if file.name.startswith("._") or file.name.endswith(".ann"):
+                    continue
+
+                # Read brat annotations for the given text file and convert example to the BigBio-KB format
+                brat_example = parsing.parse_brat_file(file)
+                kb_example = parsing.brat_parse_to_bigbio_kb(brat_example)
+                kb_example["id"] = kb_example["document_id"]
+
+                # Fix text type annotation for the converted example
+                _, text_type = self.get_document_type_and_text_type(file)
+                kb_example["passages"][0]["type"] = text_type
+
+                yield kb_example["id"], kb_example
+
+    def _to_source_example(self, input_file: Path, brat_example: Dict) -> Dict:
+        """
+        Converts an example extracted using the default brat parsing logic to the source format
+        of the given corpus.
+        """
+        document_type, text_type = self.get_document_type_and_text_type(input_file)
+
+        source_example = {
+            "document_id": brat_example["document_id"],
+            "document_type": document_type,
+            "text": brat_example["text"],
+            "text_type": text_type,
+        }
+
+        id_prefix = brat_example["document_id"] + "_"
+
+        source_example["entities"] = []
+        for entity_annotation in brat_example["text_bound_annotations"]:
+            entity_ann = entity_annotation.copy()
+
+            entity_ann["entity_id"] = id_prefix + entity_ann["id"]
+            entity_ann.pop("id")
+
+            source_example["entities"].append(entity_ann)
+
+        return source_example
+
+    def get_document_type_and_text_type(self, input_file: Path) -> Tuple[str, str]:
+        """
+        Extracts the document type (PubMed(PM) or PubMedCentral (PMC)) and the respective
+        text type (abstract for PM and sec or caption for (PMC) from the name of the given
+        file, e.g.:
+
+        PMID-9778569.txt -> ("PM", "abstract")
+
+        PMC-1274342-sec-02.txt -> ("PMC", "sec")
+
+        PMC-1592597-caption-02.ann -> ("PMC", "caption")
+
+        """
+        name_parts = str(input_file.stem).split("-")
+
+        if name_parts[0] == "PMID":
+            return "PM", "abstract"
+
+        elif name_parts[0] == "PMC":
+            return "PMC", name_parts[2]
+        else:
+            raise AssertionError(f"Unexpected file prefix {name_parts[0]}")