upload hubscripts/ddi_corpus_hub.py to hub from bigbio repo

ddi_corpus.py

@@ -0,0 +1,222 @@
+# 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 DDI corpus has been manually annotated with drugs and pharmacokinetics and
+pharmacodynamics interactions. It contains 1025 documents from two different
+sources: DrugBank database and MedLine.
+"""
+
+import os
+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 = """\
+@article{HERREROZAZO2013914,
+  title        = {
+    The DDI corpus: An annotated corpus with pharmacological substances and
+    drug-drug interactions
+  },
+  author       = {
+    María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry
+    Declerck
+  },
+  year         = 2013,
+  journal      = {Journal of Biomedical Informatics},
+  volume       = 46,
+  number       = 5,
+  pages        = {914--920},
+  doi          = {https://doi.org/10.1016/j.jbi.2013.07.011},
+  issn         = {1532-0464},
+  url          = {https://www.sciencedirect.com/science/article/pii/S1532046413001123},
+  keywords     = {Biomedical corpora, Drug interaction, Information extraction}
+}
+
+"""
+
+_DATASETNAME = "ddi_corpus"
+_DISPLAYNAME = "DDI Corpus"
+
+_DESCRIPTION = """\
+The DDI corpus has been manually annotated with drugs and pharmacokinetics and \
+pharmacodynamics interactions. It contains 1025 documents from two different \
+sources: DrugBank database and MedLine.
+"""
+
+_HOMEPAGE = "https://github.com/isegura/DDICorpus"
+
+_LICENSE = 'Creative Commons Attribution Non Commercial 4.0 International'
+
+_URLS = {
+    _DATASETNAME: "https://github.com/isegura/DDICorpus/raw/master/DDICorpus-2013(BRAT).zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class DDICorpusDataset(datasets.GeneratorBasedBuilder):
+    """DDI Corpus"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="ddi_corpus_source",
+            version=SOURCE_VERSION,
+            description="DDI Corpus source schema",
+            schema="source",
+            subset_id="ddi_corpus",
+        ),
+        BigBioConfig(
+            name="ddi_corpus_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="DDI Corpus BigBio schema",
+            schema="bigbio_kb",
+            subset_id="ddi_corpus",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ddi_corpus_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "offsets": datasets.Sequence(datasets.Value("int32")),
+                            "text": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "id": 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]:
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        standoff_dir = os.path.join(data_dir, "DDICorpusBrat")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(standoff_dir, "Train"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(standoff_dir, "Test"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: str, split: str) -> Tuple[int, Dict]:
+        if self.config.schema == "source":
+            for subdir, _, files in os.walk(filepath):
+                for file in files:
+                    # Ignore configuration files and annotation files - we just consider the brat text files
+                    if not file.endswith(".txt"):
+                        continue
+
+                    brat_example = parsing.parse_brat_file(Path(subdir) / file)
+                    source_example = self._to_source_example(brat_example)
+
+                    yield source_example["document_id"], source_example
+
+        elif self.config.schema == "bigbio_kb":
+            for subdir, _, files in os.walk(filepath):
+                for file in files:
+                    # Ignore configuration files and annotation files - we just consider the brat text files
+                    if not file.endswith(".txt"):
+                        continue
+
+                    # Read brat annotations for the given text file and convert example to the BigBio-KB format
+                    brat_example = parsing.parse_brat_file(Path(subdir) / file)
+                    kb_example = parsing.brat_parse_to_bigbio_kb(brat_example)
+                    kb_example["id"] = kb_example["document_id"]
+
+                    yield kb_example["id"], kb_example
+
+    @staticmethod
+    def _to_source_example(brat_example: Dict) -> Dict:
+        source_example = {
+            "document_id": brat_example["document_id"],
+            "text": brat_example["text"],
+            "relations": brat_example["relations"],
+        }
+
+        source_example["entities"] = []
+        for entity_annotation in brat_example["text_bound_annotations"]:
+            entity_ann = entity_annotation.copy()
+
+            source_example["entities"].append(
+                {
+                    # These are lists in the parsed output, so just take the first element to
+                    # match the source schema.
+                    "offsets": entity_annotation["offsets"][0],
+                    "text": entity_ann["text"][0],
+                    "type": entity_ann["type"],
+                    "id": entity_ann["id"],
+                }
+            )
+
+        return source_example