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

cpi.py

@@ -0,0 +1,295 @@
+# 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 compound-protein relationship (CPI) dataset consists of 2,613 sentences from abstracts containing
+annotations of proteins, small molecules, and their relationships. For further information see:
+https://pubmed.ncbi.nlm.nih.gov/32126064/ and https://github.com/KerstenDoering/CPI-Pipeline
+"""
+import xml.etree.ElementTree as ET
+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 = """\
+@article{doring2020automated,
+  title={Automated recognition of functional compound-protein relationships in literature},
+  author={D{\"o}ring, Kersten and Qaseem, Ammar and Becer, Michael and Li, Jianyu and Mishra, Pankaj and Gao, Mingjie and Kirchner, Pascal and Sauter, Florian and Telukunta, Kiran K and Moumbock, Aur{\'e}lien FA and others},
+  journal={Plos one},
+  volume={15},
+  number={3},
+  pages={e0220925},
+  year={2020},
+  publisher={Public Library of Science San Francisco, CA USA}
+}
+"""
+
+_DATASETNAME = "cpi"
+_DISPLAYNAME = "CPI"
+
+_DESCRIPTION = """\
+The compound-protein relationship (CPI) dataset consists of 2,613 sentences from abstracts containing \
+annotations of proteins, small molecules, and their relationships
+"""
+
+_HOMEPAGE = "https://github.com/KerstenDoering/CPI-Pipeline"
+
+_LICENSE = 'ISC License'
+
+_URLS = {
+    "CPI": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS.xml",
+    "CPI_IV": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS_IV.xml",
+    "CPI_NIV": "https://github.com/KerstenDoering/CPI-Pipeline/raw/master/data_sets/xml/CPI-DS_IV.xml",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.NAMED_ENTITY_DISAMBIGUATION, Tasks.RELATION_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.2"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class CpiDataset(datasets.GeneratorBasedBuilder):
+    """The compound-protein relationship (CPI) dataset"""
+
+    ENTITY_TYPE_TO_DB_NAME = {"compound": "PubChem", "protein": "UniProt"}
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="cpi_source",
+            version=SOURCE_VERSION,
+            description="CPI source schema",
+            schema="source",
+            subset_id="cpi",
+        ),
+        BigBioConfig(
+            name="cpi_iv_source",
+            version=SOURCE_VERSION,
+            description="CPI source schema - subset with interaction verbs",
+            schema="source",
+            subset_id="cpi_iv",
+        ),
+        BigBioConfig(
+            name="cpi_niv_source",
+            version=SOURCE_VERSION,
+            description="CPI source schema - subset without interaction verbs",
+            schema="source",
+            subset_id="cpi_niv",
+        ),
+        BigBioConfig(
+            name="cpi_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="CPI BigBio schema",
+            schema="bigbio_kb",
+            subset_id="cpi",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "cpi_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "document_orig_id": datasets.Value("string"),
+                    "sentences": [
+                        {
+                            "sentence_id": datasets.Value("string"),
+                            "sentence_orig_id": datasets.Value("string"),
+                            "text": datasets.Value("string"),
+                            "entities": [
+                                {
+                                    "entity_id": datasets.Value("string"),
+                                    "entity_orig_id": datasets.Sequence(datasets.Value("string")),
+                                    "type": datasets.Value("string"),
+                                    "offset": datasets.Sequence(datasets.Value("int32")),
+                                    "text": datasets.Value("string"),
+                                }
+                            ],
+                            "pairs": [
+                                {
+                                    "pair_id": datasets.Value("string"),
+                                    "e1": datasets.Value("string"),
+                                    "e2": datasets.Value("string"),
+                                    "interaction": datasets.Value("bool"),
+                                }
+                            ],
+                        }
+                    ],
+                }
+            )
+
+        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):
+        # Distinguish based on the subset id (cpi, cpi_iv, cpi_niv) which file to load
+        subset_url = _URLS[self.config.subset_id.upper()]
+        subset_file = dl_manager.download_and_extract(subset_url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"subset_file": subset_file},
+            )
+        ]
+
+    def _generate_examples(self, subset_file: Path) -> Iterator[Tuple[str, Dict]]:
+        if self.config.schema == "source":
+            for doc_id, document in self._read_source_examples(subset_file):
+                yield doc_id, document
+
+        elif self.config.name == "cpi_bigbio_kb":
+            # Note: The sentences in a CPI document does not (necessarily) occur consecutive in
+            # the original publication. Nevertheless, in this implementation we capture all sentences
+            # of a document in one kb-schema document to explicitly model documents.
+
+            # Transform each source-schema document to kb-schema document
+            for doc_id, source_document in self._read_source_examples(subset_file):
+                sentence_offset = 0
+                passages = []
+                entities = []
+                relations = []
+
+                # Transform all sentences to kb-schema sentences
+                for source_sentence in source_document["sentences"]:
+                    text = source_sentence["text"]
+                    passages.append(
+                        {
+                            "id": source_sentence["sentence_id"],
+                            "text": [text],
+                            "offsets": [[sentence_offset + 0, sentence_offset + len(text)]],
+                            "type": "",
+                        }
+                    )
+
+                    # Transform source-schema entities to kb-schema entities
+                    for source_entity in source_sentence["entities"]:
+                        db_name = self.ENTITY_TYPE_TO_DB_NAME[source_entity["type"]]
+
+                        entity_offset = source_entity["offset"]
+                        entity_offset = [sentence_offset + entity_offset[0], sentence_offset + entity_offset[1]]
+
+                        entities.append(
+                            {
+                                "id": source_entity["entity_id"],
+                                "type": source_entity["type"],
+                                "text": [source_entity["text"]],
+                                "offsets": [entity_offset],
+                                "normalized": [
+                                    {"db_name": db_name, "db_id": db_id} for db_id in source_entity["entity_orig_id"]
+                                ],
+                            }
+                        )
+
+                    # Transform source-schema pairs to kb-schema relations
+                    for source_pair in source_sentence["pairs"]:
+                        # Ignore pairs that are annotated to be not in a relationship!
+                        if not source_pair["interaction"]:
+                            continue
+
+                        relations.append(
+                            {
+                                "id": source_pair["pair_id"],
+                                "type": "compound-protein-interaction",
+                                "arg1_id": source_pair["e1"],
+                                "arg2_id": source_pair["e2"],
+                                "normalized": [],
+                            }
+                        )
+
+                    sentence_offset += len(text) + 1
+
+                kb_document = {
+                    "id": source_document["document_id"],
+                    "document_id": source_document["document_orig_id"],
+                    "passages": passages,
+                    "entities": entities,
+                    "relations": relations,
+                    "events": [],
+                    "coreferences": [],
+                }
+
+                yield source_document["document_id"], kb_document
+
+    def _read_source_examples(self, input_file: Path) -> Iterator[Tuple[str, Dict]]:
+        """
+        Reads all instances of the given input file and parses them into the source format.
+        """
+        root = ET.parse(input_file)
+        for document in root.iter("document"):
+            sentences = []
+            for sentence in document.iter("sentence"):
+                entities = []
+                for entity in sentence.iter("entity"):
+                    char_offsets = entity.attrib["charOffset"].split("-")
+                    start, end = int(char_offsets[0]), int(char_offsets[1])
+
+                    entities.append(
+                        {
+                            "entity_id": entity.attrib["id"],
+                            "entity_orig_id": entity.attrib["origId"].split(","),
+                            "type": entity.attrib["type"],
+                            "text": entity.attrib["text"],
+                            "offset": [start, end],
+                        }
+                    )
+
+                pairs = []
+                for pair in sentence.iter("pair"):
+                    pairs.append(
+                        {
+                            "pair_id": pair.attrib["id"],
+                            "e1": pair.attrib["e1"],
+                            "e2": pair.attrib["e2"],
+                            "interaction": pair.attrib["interaction"].lower() == "true",
+                        }
+                    )
+
+                sentences.append(
+                    {
+                        "sentence_id": sentence.attrib["id"],
+                        "sentence_orig_id": sentence.attrib["origId"],
+                        "text": sentence.attrib["text"],
+                        "entities": entities,
+                        "pairs": pairs,
+                    }
+                )
+
+            document_dict = {
+                "document_id": document.attrib["id"],
+                "document_orig_id": document.attrib["origId"],
+                "sentences": sentences,
+            }
+
+            yield document.attrib["id"], document_dict