upload hubscripts/iepa_hub.py to hub from bigbio repo

iepa.py

@@ -0,0 +1,287 @@
+# 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 IEPA benchmark PPI corpus is designed for relation extraction. It was
+created from 303 PubMed abstracts, each of which contains a specific pair of
+co-occurring chemicals.
+"""
+
+# Comment from Author
+# BigBio schema fixes offsets of entities to an offset where 0 is the start of the document.
+# (In source offsets of entities start from 0 for each passage in document)
+# Offsets of entities in source remain unchanged.
+
+import xml.dom.minidom as xml
+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{ding2001mining,
+  title    = "Mining {MEDLINE}: abstracts, sentences, or phrases?",
+  author   = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E",
+  journal  = "Pac Symp Biocomput",
+  pages    = "326--337",
+  year     =  2002,
+  address  = "United States",
+  language = "en"
+}
+"""
+
+_DATASETNAME = "iepa"
+_DISPLAYNAME = "IEPA"
+
+_DESCRIPTION = """\
+The IEPA benchmark PPI corpus is designed for relation extraction. It was \
+created from 303 PubMed abstracts, each of which contains a specific pair of \
+co-occurring chemicals.
+"""
+
+_HOMEPAGE = "http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/metalrt/ppi-dataset/master/csv_output/IEPA-train.xml",
+        "test": "https://raw.githubusercontent.com/metalrt/ppi-dataset/master/csv_output/IEPA-test.xml",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_BIGBIO_VERSION = "1.0.0"
+
+
+class IepaDataset(datasets.GeneratorBasedBuilder):
+    """The IEPA benchmark PPI corpus is designed for relation extraction."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="iepa_source",
+            version=SOURCE_VERSION,
+            description="IEPA source schema",
+            schema="source",
+            subset_id="iepa",
+        ),
+        BigBioConfig(
+            name="iepa_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="IEPA BigBio schema",
+            schema="bigbio_kb",
+            subset_id="iepa",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "iepa_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "PMID": datasets.Value("string"),
+                    "origID": datasets.Value("string"),
+                    "sentences": [
+                        {
+                            "id": datasets.Value("string"),
+                            "origID": datasets.Value("string"),
+                            "offsets": [datasets.Value("int32")],
+                            "text": datasets.Value("string"),
+                            "entities": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "origID": datasets.Value("string"),
+                                    "text": datasets.Value("string"),
+                                    "offsets": [datasets.Value("int32")],
+                                }
+                            ],
+                            "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)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        collection = xml.parse(filepath).documentElement
+
+        if self.config.schema == "source":
+            for id, document in self._parse_documents(collection):
+                yield id, document
+
+        elif self.config.schema == "bigbio_kb":
+            for id, document in self._parse_documents(collection):
+                yield id, self._source_to_bigbio(document)
+
+    def _parse_documents(self, collection):
+        for document in collection.getElementsByTagName("document"):
+            pmid_doc = self._strict_get_attribute(document, "PMID")
+            id_doc = self._strict_get_attribute(document, "id")
+            origID_doc = self._strict_get_attribute(document, "origID")
+            sentences = []
+            for sentence in document.getElementsByTagName("sentence"):
+                offsets_sent = self._strict_get_attribute(sentence, "charOffset").split(
+                    "-"
+                )
+                id_sent = self._strict_get_attribute(sentence, "id")
+                origID_sent = self._strict_get_attribute(sentence, "origID")
+                text_sent = self._strict_get_attribute(sentence, "text")
+
+                entities = []
+                for entity in sentence.getElementsByTagName("entity"):
+                    id_ent = self._strict_get_attribute(entity, "id")
+                    origID_ent = self._strict_get_attribute(entity, "origID")
+                    text_ent = self._strict_get_attribute(entity, "text")
+                    offsets_ent = self._strict_get_attribute(
+                        entity, "charOffset"
+                    ).split("-")
+                    entities.append(
+                        {
+                            "id": id_ent,
+                            "origID": origID_ent,
+                            "text": text_ent,
+                            "offsets": offsets_ent,
+                        }
+                    )
+
+                interactions = []
+                for interaction in sentence.getElementsByTagName("interaction"):
+                    id_int = self._strict_get_attribute(interaction, "id")
+                    e1_int = self._strict_get_attribute(interaction, "e1")
+                    e2_int = self._strict_get_attribute(interaction, "e2")
+                    type_int = self._strict_get_attribute(interaction, "type")
+                    interactions.append(
+                        {"id": id_int, "e1": e1_int, "e2": e2_int, "type": type_int}
+                    )
+
+                sentences.append(
+                    {
+                        "id": id_sent,
+                        "origID": origID_sent,
+                        "offsets": offsets_sent,
+                        "text": text_sent,
+                        "entities": entities,
+                        "interactions": interactions,
+                    }
+                )
+            yield id_doc, {
+                "id": id_doc,
+                "PMID": pmid_doc,
+                "origID": origID_doc,
+                "sentences": sentences,
+            }
+
+    def _strict_get_attribute(self, element, key):
+        if element.hasAttribute(key):
+            return element.getAttribute(key)
+        else:
+            raise ValueError(f"No such key exists in element: {element.tagName} {key}")
+
+    def _source_to_bigbio(self, document_):
+        document = {}
+        document["id"] = document_["id"]
+        document["document_id"] = document_["PMID"]
+
+        passages = []
+        entities = []
+        relations = []
+        for sentence_ in document_["sentences"]:
+            for entity_ in sentence_["entities"]:
+                entity_["type"] = ""
+                entity_["normalized"] = []
+                entity_.pop("origID")
+                entity_["text"] = [entity_["text"]]
+                entity_["offsets"] = [
+                    [
+                        int(sentence_["offsets"][0]) + int(entity_["offsets"][0]),
+                        int(sentence_["offsets"][0]) + int(entity_["offsets"][1]),
+                    ]
+                ]
+                entities.append(entity_)
+            for relation_ in sentence_["interactions"]:
+                relation_["arg1_id"] = relation_.pop("e1")
+                relation_["arg2_id"] = relation_.pop("e2")
+                relation_["normalized"] = []
+                relations.append(relation_)
+
+            sentence_.pop("entities")
+            sentence_.pop("interactions")
+            sentence_.pop("origID")
+            sentence_["type"] = ""
+            sentence_["text"] = [sentence_["text"]]
+            sentence_["offsets"] = [sentence_["offsets"]]
+            passages.append(sentence_)
+
+        document["passages"] = passages
+        document["entities"] = entities
+        document["relations"] = relations
+        document["events"] = []
+        document["coreferences"] = []
+        return document