upload hub_repos/drugprot/drugprot.py to hub from bigbio repo

drugprot.py

@@ -0,0 +1,260 @@
+# 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 DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and (b) all binary relationships
+between them corresponding to a specific set of biologically relevant relation types. The corpus was introduced
+in context of the BioCreative VII Track 1 (Text mining drug and chemical-protein interactions).
+
+For further information see:
+https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/
+"""
+import collections
+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 = """\
+@inproceedings{miranda2021overview,
+  title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of \
+        drug-gene/protein relations},
+  author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso \
+        and Krallinger, Martin},
+  booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop},
+  year={2021}
+}
+"""
+
+_DATASETNAME = "drugprot"
+_DISPLAYNAME = "DrugProt"
+
+
+_DESCRIPTION = """\
+The DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and (b) all binary relationships \
+between them corresponding to a specific set of biologically relevant relation types.
+"""
+
+_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {_DATASETNAME: "https://zenodo.org/record/5119892/files/drugprot-training-development-test-background.zip?download=1"}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.2"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class DrugProtDataset(datasets.GeneratorBasedBuilder):
+    """
+        The DrugProt corpus consists of a) expert-labelled chemical and gene mentions, and \
+        (b) all binary relationships between them.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="drugprot_source",
+            version=SOURCE_VERSION,
+            description="DrugProt source schema",
+            schema="source",
+            subset_id="drugprot",
+        ),
+        BigBioConfig(
+            name="drugprot_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="DrugProt BigBio schema",
+            schema="bigbio_kb",
+            subset_id="drugprot",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "drugprot_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "offset": datasets.Sequence(datasets.Value("int32")),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arg1_id": datasets.Value("string"),
+                            "arg2_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):
+        urls = _URLS[_DATASETNAME]
+        data_dir = Path(dl_manager.download_and_extract(urls))
+        data_dir = data_dir / "drugprot-gs-training-development"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "split": "training"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": data_dir, "split": "development"},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path, split: str) -> Iterator[Tuple[str, Dict]]:
+        if self.config.name == "drugprot_source":
+            documents = self._read_source_examples(data_dir, split)
+            for document_id, document in documents.items():
+                yield document_id, document
+
+        elif self.config.name == "drugprot_bigbio_kb":
+            documents = self._read_source_examples(data_dir, split)
+            for document_id, document in documents.items():
+                yield document_id, self._transform_source_to_kb(document)
+
+    def _read_source_examples(self, input_dir: Path, split: str) -> Dict:
+        """ """
+        split_dir = input_dir / split
+        abstracts_file = split_dir / f"drugprot_{split}_abstracs.tsv"
+        entities_file = split_dir / f"drugprot_{split}_entities.tsv"
+        relations_file = split_dir / f"drugprot_{split}_relations.tsv"
+
+        document_to_entities = collections.defaultdict(list)
+        for line in entities_file.read_text().splitlines():
+            columns = line.split("\t")
+            document_id = columns[0]
+
+            document_to_entities[document_id].append(
+                {
+                    "id": document_id + "_" + columns[1],
+                    "type": columns[2],
+                    "offset": [columns[3], columns[4]],
+                    "text": columns[5],
+                }
+            )
+
+        document_to_relations = collections.defaultdict(list)
+        for line in relations_file.read_text().splitlines():
+            columns = line.split("\t")
+            document_id = columns[0]
+
+            document_relations = document_to_relations[document_id]
+
+            document_relations.append(
+                {
+                    "id": document_id + "_" + str(len(document_relations)),
+                    "type": columns[1],
+                    "arg1_id": document_id + "_" + columns[2][5:],
+                    "arg2_id": document_id + "_" + columns[3][5:],
+                }
+            )
+
+        document_to_source = {}
+        for line in abstracts_file.read_text().splitlines():
+            document_id, title, abstract = line.split("\t")
+
+            document_to_source[document_id] = {
+                "document_id": document_id,
+                "title": title,
+                "abstract": abstract,
+                "text": " ".join([title, abstract]),
+                "entities": document_to_entities[document_id],
+                "relations": document_to_relations[document_id],
+            }
+
+        return document_to_source
+
+    def _transform_source_to_kb(self, source_document: Dict) -> Dict:
+        document_id = source_document["document_id"]
+
+        offset = 0
+        passages = []
+        for text_field in ["title", "abstract"]:
+            text = source_document[text_field]
+            passages.append(
+                {
+                    "id": document_id + "_" + text_field,
+                    "type": text_field,
+                    "text": [text],
+                    "offsets": [[offset, offset + len(text)]],
+                }
+            )
+            offset += len(text) + 1
+
+        entities = [
+            {
+                "id": entity["id"],
+                "type": entity["type"],
+                "text": [entity["text"]],
+                "offsets": [entity["offset"]],
+                "normalized": [],
+            }
+            for entity in source_document["entities"]
+        ]
+
+        relations = [
+            {
+                "id": relation["id"],
+                "type": relation["type"],
+                "arg1_id": relation["arg1_id"],
+                "arg2_id": relation["arg2_id"],
+                "normalized": [],
+            }
+            for relation in source_document["relations"]
+        ]
+
+        return {
+            "id": document_id,
+            "document_id": document_id,
+            "passages": passages,
+            "entities": entities,
+            "relations": relations,
+            "events": [],
+            "coreferences": [],
+        }