upload hubscripts/an_em_hub.py to hub from bigbio repo

an_em.py

@@ -0,0 +1,302 @@
+# 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.
+"""
+AnEM corpus is a domain- and species-independent resource manually annotated for anatomical
+entity mentions using a fine-grained classification system. The corpus consists of 500 documents
+(over 90,000 words) selected randomly from citation abstracts and full-text papers with
+the aim of making the corpus representative of the entire available biomedical scientific
+literature. The corpus annotation covers mentions of both healthy and pathological anatomical
+entities and contains over 3,000 annotated mentions.
+"""
+
+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 = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{ohta-etal-2012-open,
+  author    = {Ohta, Tomoko and Pyysalo, Sampo and Tsujii, Jun{'}ichi and Ananiadou, Sophia},
+  title     = {Open-domain Anatomical Entity Mention Detection},
+  journal   = {},
+  volume    = {W12-43},
+  year      = {2012},
+  url       = {https://aclanthology.org/W12-4304},
+  doi       = {},
+  biburl    = {},
+  bibsource = {},
+  publisher = {Association for Computational Linguistics}
+}
+"""
+
+_DATASETNAME = "an_em"
+_DISPLAYNAME = "AnEM"
+
+_DESCRIPTION = """\
+AnEM corpus is a domain- and species-independent resource manually annotated for anatomical
+entity mentions using a fine-grained classification system. The corpus consists of 500 documents
+(over 90,000 words) selected randomly from citation abstracts and full-text papers with
+the aim of making the corpus representative of the entire available biomedical scientific
+literature. The corpus annotation covers mentions of both healthy and pathological anatomical
+entities and contains over 3,000 annotated mentions.
+"""
+
+
+_HOMEPAGE = "http://www.nactem.ac.uk/anatomy/"
+
+_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported'
+
+_URLS = {
+    _DATASETNAME: "http://www.nactem.ac.uk/anatomy/data/AnEM-1.0.4.tar.gz",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.COREFERENCE_RESOLUTION,
+    Tasks.RELATION_EXTRACTION,
+]
+
+_SOURCE_VERSION = "1.0.4"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class AnEMDataset(datasets.GeneratorBasedBuilder):
+    """Anatomical Entity Mention (AnEM) corpus"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="an_em_source",
+            version=SOURCE_VERSION,
+            description="AnEM source schema",
+            schema="source",
+            subset_id="an_em",
+        ),
+        BigBioConfig(
+            name="an_em_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="AnEM BigBio schema",
+            schema="bigbio_kb",
+            subset_id="an_em",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "an_em_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "document_type": datasets.Value("string"),
+                    "text_type": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "entity_id": datasets.Value("string"),
+                        }
+                    ],
+                    "equivalences": [
+                        {
+                            "entity_id": datasets.Value("string"),
+                            "ref_ids": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "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"),
+                        }
+                    ],
+                }
+            )
+
+        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[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+        all_data = data_dir / "AnEM-1.0.4" / "standoff"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": all_data,
+                    "split_path": data_dir
+                    / "AnEM-1.0.4"
+                    / "development"
+                    / "train-files.list",
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": all_data,
+                    "split_path": data_dir / "AnEM-1.0.4" / "test" / "test-files.list",
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": all_data,
+                    "split_path": data_dir
+                    / "AnEM-1.0.4"
+                    / "development"
+                    / "test-files.list",
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split_path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(split_path, "r") as sp:
+            split_list = [line.rstrip() for line in sp]
+
+        if self.config.schema == "source":
+            for file in filepath.iterdir():
+
+                # Use brat text files and consider files in the provided split list
+                if (file.suffix != ".txt") or (file.stem not in split_list):
+                    continue
+                brat_parsed = parse.parse_brat_file(file)
+                source_example = self._brat_to_source(file, brat_parsed)
+
+                yield source_example["document_id"], source_example
+
+        elif self.config.schema == "bigbio_kb":
+            for file in filepath.iterdir():
+
+                # Use brat text files and consider files in the provided split list
+                if (file.suffix != ".txt") or (file.stem not in split_list):
+                    continue
+                brat_parsed = parse.parse_brat_file(file)
+                bigbio_kb_example = parse.brat_parse_to_bigbio_kb(brat_parsed)
+
+                bigbio_kb_example["id"] = bigbio_kb_example["document_id"]
+
+                doc_type, text_type = self.get_document_type_and_text_type(file)
+                bigbio_kb_example["passages"][0]["type"] = text_type
+
+                yield bigbio_kb_example["id"], bigbio_kb_example
+
+    def _brat_to_source(self, filepath, brat_example):
+        """
+        Converts parsed brat example to source schema example
+        """
+        document_type, text_type = self.get_document_type_and_text_type(filepath)
+
+        source_example = {
+            "document_id": brat_example["document_id"],
+            "text": brat_example["text"],
+            "document_type": document_type,
+            "text_type": text_type,
+            "entities": [
+                {
+                    "offsets": brat_entity["offsets"],
+                    "text": brat_entity["text"],
+                    "type": brat_entity["type"],
+                    "entity_id": f"{brat_example['document_id']}_{brat_entity['id']}",
+                }
+                for brat_entity in brat_example["text_bound_annotations"]
+            ],
+            "equivalences": [
+                {
+                    "entity_id": brat_entity["id"],
+                    "ref_ids": [
+                        f"{brat_example['document_id']}_{ids}"
+                        for ids in brat_entity["ref_ids"]
+                    ],
+                }
+                for brat_entity in brat_example["equivalences"]
+            ],
+            "relations": [
+                {
+                    "id": f"{brat_example['document_id']}_{brat_entity['id']}",
+                    "head": {
+                        "ref_id": f"{brat_example['document_id']}_{brat_entity['head']['ref_id']}",
+                        "role": brat_entity["head"]["role"],
+                    },
+                    "tail": {
+                        "ref_id": f"{brat_example['document_id']}_{brat_entity['tail']['ref_id']}",
+                        "role": brat_entity["tail"]["role"],
+                    },
+                    "type": brat_entity["type"],
+                }
+                for brat_entity in brat_example["relations"]
+            ],
+        }
+
+        return source_example
+
+    def get_document_type_and_text_type(self, input_file: Path) -> Tuple[str, str]:
+        """
+        Implementation used from
+        https://github.com/bigscience-workshop/biomedical/blob/master/biodatasets/anat_em/anat_em.py
+
+        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]}")