upload hubscripts/hprd50_hub.py to hub from bigbio repo

hprd50.py

@@ -0,0 +1,332 @@
+# 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.
+
+"""
+HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
+referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
+splitting each abstract into sentences, and in each sentence there may be entities and
+interactions between those entities. In this particular dataset, entities are all
+proteins and interactions are thus protein-protein interactions.
+
+Moreover, all entities are normalized to the HPRD database. These normalized terms are
+stored in each entity's 'type' attribute in the source XML. This means the dataset can
+determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
+entity.
+
+Because the dataset contains entities and relations, it is suitable for Named Entity
+Recognition and Relation Extraction.
+"""
+
+import os
+from glob import glob
+from typing import Dict, List, Tuple
+from xml.etree import ElementTree
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+# TODO: Add BibTeX citation
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{fundel2007relex,
+  title={RelEx—Relation extraction using dependency parse trees},
+  author={Fundel, Katrin and K{\"u}ffner, Robert and Zimmer, Ralf},
+  journal={Bioinformatics},
+  volume={23},
+  number={3},
+  pages={365--371},
+  year={2007},
+  publisher={Oxford University Press}
+}
+"""
+
+_DATASETNAME = "hprd50"
+_DISPLAYNAME = "HPRD50"
+
+_DESCRIPTION = """\
+HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
+referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
+splitting each abstract into sentences, and in each sentence there may be entities and
+interactions between those entities. In this particular dataset, entities are all
+proteins and interactions are thus protein-protein interactions.
+
+Moreover, all entities are normalized to the HPRD database. These normalized terms are
+stored in each entity's 'type' attribute in the source XML. This means the dataset can
+determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
+entity.
+
+Because the dataset contains entities and relations, it is suitable for Named Entity
+Recognition and Relation Extraction.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://github.com/metalrt/ppi-dataset/zipball/master",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.RELATION_EXTRACTION,
+    Tasks.NAMED_ENTITY_RECOGNITION,
+]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+def parse_xml_source(document_trees):
+    entries = []
+    for doc in document_trees:
+        document = {
+            "id": doc.get("id"),
+            "origId": doc.get("origId"),
+            "set": doc.get("test"),
+            "sentences": [],
+        }
+        for s in doc.findall("sentence"):
+            sentence = {
+                "id": s.get("id"),
+                "origId": s.get("origId"),
+                "charOffset": s.get("charOffset"),
+                "text": s.get("text"),
+                "entities": [],
+                "interactions": [],
+            }
+
+            for e in s.findall("entity"):
+                entity = {
+                    "id": e.get("id"),
+                    "origId": e.get("origId"),
+                    "charOffset": e.get("charOffset"),
+                    "text": e.get("text"),
+                    "type": e.get("type"),
+                }
+
+                sentence["entities"].append(entity)
+
+            for i in s.findall("interaction"):
+                interaction = {
+                    "id": i.get("id"),
+                    "e1": i.get("e1"),
+                    "e2": i.get("e2"),
+                    "type": i.get("type"),
+                }
+                sentence["interactions"].append(interaction)
+
+            document["sentences"].append(sentence)
+
+        entries.append(document)
+    return entries
+
+
+def parse_xml_bigbio_kb(document_trees):
+    entries = []
+    for doc in document_trees:
+        document = {
+            "id": doc.get("id"),
+            "document_id": doc.get("origId"),
+            "passages": [],
+            "entities": [],
+            "relations": [],
+            "events": [],
+            "coreferences": [],
+        }
+        for s in doc.findall("sentence"):
+
+            offset = s.get("charOffset").split("-")
+            start = int(offset[0])
+            end = int(offset[1])
+
+            passage = {
+                "id": s.get("id"),
+                "type": "sentence",
+                "text": [s.get("text")],
+                "offsets": [[start, end]],
+            }
+
+            document["passages"].append(passage)
+
+            for e in s.findall("entity"):
+
+                offset = e.get("charOffset").split("-")
+                start = int(offset[0])
+                end = int(offset[1])
+
+                entity = {
+                    "id": e.get("id"),
+                    "text": [e.get("text")],
+                    "offsets": [[start, end]],
+                    "type": "protein",
+                    "normalized": [{"db_name": "HPRD", "db_id": e.get("type")}],
+                }
+
+                document["entities"].append(entity)
+
+            for i in s.findall("interaction"):
+                relation = {
+                    "id": i.get("id"),
+                    "arg1_id": i.get("e1"),
+                    "arg2_id": i.get("e2"),
+                    "type": i.get("type"),
+                    "normalized": [],
+                }
+                document["relations"].append(relation)
+
+        entries.append(document)
+    return entries
+
+
+class HPRD50Dataset(datasets.GeneratorBasedBuilder):
+    """
+    HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
+    referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
+    splitting each abstract into sentences, and in each sentence there may be entities and
+    interactions between those entities. In this particular dataset, entities are all
+    proteins and interactions are thus protein-protein interactions.
+
+    Moreover, all entities are normalized to the HPRD database. These normalized terms are
+    stored in each entity's 'type' attribute in the source XML. This means the dataset can
+    determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
+    entity.
+
+    Because the dataset contains entities and relations, it is suitable for Named Entity
+    Recognition and Relation Extraction.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="hprd50_source",
+            version=SOURCE_VERSION,
+            description="hprd50 source schema",
+            schema="source",
+            subset_id="hprd50",
+        ),
+        BigBioConfig(
+            name="hprd50_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="hprd50 BigBio schema",
+            schema="bigbio_kb",
+            subset_id="hprd50",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "hprd50_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "origId": datasets.Value("string"),
+                    "set": datasets.Value("string"),
+                    "sentences": [
+                        {
+                            "id": datasets.Value("string"),
+                            "origId": datasets.Value("string"),
+                            "charOffset": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "entities": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "origId": datasets.Value("string"),
+                                    "charOffset": datasets.Value("string"),
+                                    "text": datasets.Value("string"),
+                                    "type": datasets.Value("string"),
+                                }
+                            ],
+                            "interactions": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "e1": datasets.Value("string"),
+                                    "e2": 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 = dl_manager.download_and_extract(urls)
+        # Files are actually a few levels down, under this subdirectory, and
+        # intermediate directory names get hashed so this is the easiest way to find it.
+        data_dir = glob(f"{data_dir}/**/csv_output")[0]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "HPRD50-train.xml"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "HPRD50-test.xml"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, "r") as f:
+            content = f.read()
+
+        tree = ElementTree.fromstring(content)
+        documents = tree.findall("document")
+
+        if self.config.schema == "source":
+            entries = parse_xml_source(documents)
+            for key, example in enumerate(entries):
+                yield key, example
+
+        elif self.config.schema == "bigbio_kb":
+            entries = parse_xml_bigbio_kb(documents)
+            for key, example in enumerate(entries):
+                yield key, example
+
+
+# This template is based on the following template from the datasets package:
+# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py