upload hubscripts/bioinfer_hub.py to hub from bigbio repo

bioinfer.py

@@ -0,0 +1,259 @@
+# 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 authors present BioInfer (Bio Information Extraction Resource), a new public
+resource providing an annotated corpus of biomedical English. We describe an
+annotation scheme capturing named entities and their relationships along with a
+dependency analysis of sentence syntax. We further present ontologies defining
+the types of entities and relationships annotated in the corpus. Currently, the
+corpus contains 1100 sentences from abstracts of biomedical research articles
+annotated for relationships, named entities, as well as syntactic dependencies.
+"""
+
+import os
+import xml.etree.ElementTree as ET
+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{pyysalo2007bioinfer,
+  title        = {BioInfer: a corpus for information extraction in the biomedical domain},
+  author       = {
+    Pyysalo, Sampo and Ginter, Filip and Heimonen, Juho and Bj{\"o}rne, Jari
+    and Boberg, Jorma and J{\"a}rvinen, Jouni and Salakoski, Tapio
+  },
+  year         = 2007,
+  journal      = {BMC bioinformatics},
+  publisher    = {BioMed Central},
+  volume       = 8,
+  number       = 1,
+  pages        = {1--24}
+}
+"""
+
+_DATASETNAME = "bioinfer"
+_DISPLAYNAME = "BioInfer"
+
+_DESCRIPTION = """\
+A corpus targeted at protein, gene, and RNA relationships which serves as a
+resource for the development of information extraction systems and their
+components such as parsers and domain analyzers. Currently, the corpus contains
+1100 sentences from abstracts of biomedical research articles annotated for
+relationships, named entities, as well as syntactic dependencies.
+"""
+
+_HOMEPAGE = "https://github.com/metalrt/ppi-dataset"
+
+_LICENSE = 'Creative Commons Attribution 2.0 Generic'
+
+_URLS = {
+    _DATASETNAME: "https://github.com/metalrt/ppi-dataset/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION, Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class BioinferDataset(datasets.GeneratorBasedBuilder):
+    """
+    1100 sentences from abstracts of biomedical research articles annotated
+    for relationships, named entities, as well as syntactic dependencies.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bioinfer_source",
+            version=SOURCE_VERSION,
+            description="BioInfer source schema",
+            schema="source",
+            subset_id="bioinfer",
+        ),
+        BigBioConfig(
+            name="bioinfer_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="BioInfer BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bioinfer",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bioinfer_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "offsets": [[datasets.Value("int32")]],
+                            "text": [datasets.Value("string")],
+                            "type": datasets.Value("string"),
+                            "normalized": [
+                                {
+                                    "db_name": datasets.Value("string"),
+                                    "db_id": datasets.Value("string"),
+                                }
+                            ],
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arg1_id": datasets.Value("string"),
+                            "arg2_id": datasets.Value("string"),
+                            "normalized": [
+                                {
+                                    "db_name": datasets.Value("string"),
+                                    "db_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) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "ppi-dataset-master/csv_output/BioInfer-train.xml"
+                    ),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, "ppi-dataset-master/csv_output/BioInfer-test.xml"
+                    ),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        tree = ET.parse(filepath)
+        root = tree.getroot()
+        if self.config.schema == "source":
+            for guid, sentence in enumerate(root.iter("sentence")):
+                example = self._create_example(sentence)
+                example["text"] = sentence.attrib["text"]
+                example["type"] = "Sentence"
+                yield guid, example
+
+        elif self.config.schema == "bigbio_kb":
+            for guid, sentence in enumerate(root.iter("sentence")):
+                example = self._create_example(sentence)
+                example["passages"] = [
+                    {
+                        "id": f"{sentence.attrib['id']}__text",
+                        "type": "Sentence",
+                        "text": [sentence.attrib["text"]],
+                        "offsets": [(0, len(sentence.attrib["text"]))],
+                    }
+                ]
+                example["events"] = []
+                example["coreferences"] = []
+                example["id"] = guid
+                yield guid, example
+
+    def _create_example(self, sentence):
+        example = {}
+        example["document_id"] = sentence.attrib["id"]
+        example["entities"] = []
+        example["relations"] = []
+        for tag in sentence:
+            if tag.tag == "entity":
+                example["entities"].append(self._add_entity(tag))
+            elif tag.tag == "interaction":
+                example["relations"].append(self._add_interaction(tag))
+            else:
+                raise ValueError(f"unknown tags: {tag.tag}")
+        return example
+
+    @staticmethod
+    def _add_entity(entity):
+        offsets = [
+            [int(o) for o in offset.split("-")]
+            for offset in entity.attrib["charOffset"].split(",")
+        ]
+        # For multiple offsets, split entity text accordingly
+        if len(offsets) > 1:
+            text = []
+            i = 0
+            for start, end in offsets:
+                chunk_len = end - start
+                text.append(entity.attrib["text"][i : chunk_len + i])
+                i += chunk_len
+                while (
+                    i < len(entity.attrib["text"]) and entity.attrib["text"][i] == " "
+                ):
+                    i += 1
+        else:
+            text = [entity.attrib["text"]]
+        return {
+            "id": entity.attrib["id"],
+            "offsets": offsets,
+            "text": text,
+            "type": entity.attrib["type"],
+            "normalized": {},
+        }
+
+    @staticmethod
+    def _add_interaction(interaction):
+        return {
+            "id": interaction.attrib["id"],
+            "type": interaction.attrib["type"],
+            "arg1_id": interaction.attrib["e1"],
+            "arg2_id": interaction.attrib["e2"],
+            "normalized": {},
+        }