upload hubscripts/verspoor_2013_hub.py to hub from bigbio repo

verspoor_2013.py

@@ -0,0 +1,266 @@
+# 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.
+
+"""
+This dataset contains annotations for a small corpus of full text journal
+publications on the subject of inherited colorectal cancer. It is suitable for
+Named Entity Recognition and Relation Extraction tasks. It uses the Variome
+Annotation Schema,  a schema that aims to capture the core concepts and
+relations relevant to cataloguing  and interpreting human genetic variation and
+its relationship to disease, as described in the published literature. The
+schema was inspired by the needs of the database curators of the International
+Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is
+intended to have application to genetic variation information in a range of
+diseases.
+"""
+
+from pathlib import Path
+from shutil import rmtree
+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 = """\
+@article{verspoor2013annotating,
+  title        = {Annotating the biomedical literature for the human variome},
+  author       = {
+    Verspoor, Karin and Jimeno Yepes, Antonio and Cavedon, Lawrence and
+    McIntosh, Tara and Herten-Crabb, Asha and Thomas, Zo{"e} and Plazzer,
+    John-Paul
+  },
+  year         = 2013,
+  journal      = {Database},
+  publisher    = {Oxford Academic},
+  volume       = 2013
+}
+"""
+
+_DATASETNAME = "verspoor_2013"
+_DISPLAYNAME = "Verspoor 2013"
+
+_DESCRIPTION = """\
+This dataset contains annotations for a small corpus of full text journal \
+publications on the subject of inherited colorectal cancer. It is suitable for \
+Named Entity Recognition and Relation Extraction tasks. It uses the Variome \
+Annotation Schema,  a schema that aims to capture the core concepts and \
+relations relevant to cataloguing  and interpreting human genetic variation and \
+its relationship to disease, as described in the published literature. The \
+schema was inspired by the needs of the database curators of the International \
+Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is \
+intended to have application to genetic variation information in a range of \
+diseases.
+"""
+
+
+_HOMEPAGE = "NA"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = ["http://github.com/rockt/SETH/zipball/master/"]
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.RELATION_EXTRACTION,
+]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class Verspoor2013Dataset(datasets.GeneratorBasedBuilder):
+    """\
+    This dataset contains annotations for a small corpus of full text journal publications
+    on the subject of inherited colorectal cancer. It is suitable for Named Entity Recognition and
+    Relation Extraction tasks. It uses the Variome Annotation Schema,  a schema that aims to
+    capture the core concepts and relations relevant to cataloguing  and interpreting human
+    genetic variation and its relationship to disease, as described in the published literature.
+    The schema was inspired by the needs of the database curators of the International Society
+    for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have
+    application to genetic variation information in a range of diseases.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="verspoor_2013_source",
+            version=SOURCE_VERSION,
+            description="verspoor_2013 source schema",
+            schema="source",
+            subset_id="verspoor_2013",
+        ),
+        BigBioConfig(
+            name="verspoor_2013_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="verspoor_2013 BigBio schema",
+            schema="bigbio_kb",
+            subset_id="verspoor_2013",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "verspoor_2013_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        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,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        # Download gets entire git repo containing unused data from other datasets
+        repo_dir = Path(dl_manager.download_and_extract(_URLS[0]))
+        data_dir = repo_dir / "data"
+        data_dir.mkdir(exist_ok=True)
+
+        # Find the relevant files from Verspor2013 and move them to a new directory
+        verspoor_files = repo_dir.glob("*/*/*Verspoor2013/**/*")
+        for file in verspoor_files:
+            if file.is_file() and "readme" not in str(file):
+                file.rename(data_dir / file.name)
+
+        # Delete all unused files and directories from the original download
+        for x in repo_dir.glob("[!data]*"):
+            if x.is_file():
+                x.unlink()
+            elif x.is_dir():
+                rmtree(x)
+
+        data_files = {"text_files": list(data_dir.glob("*.txt"))}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_files": data_files,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, data_files, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            txt_files = data_files["text_files"]
+            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":
+            txt_files = data_files["text_files"]
+            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}")