upload hubscripts/pcr_hub.py to hub from bigbio repo

pcr.py

@@ -0,0 +1,229 @@
+# 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.
+"""
+A corpus for plant and chemical entities and for the relationships between them.
+The corpus contains 2218 plant and chemical entities and 600 plant-chemical
+relationships which are drawn from 1109 sentences in 245 PubMed abstracts.
+"""
+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{choi2016corpus,
+  title        = {A corpus for plant-chemical relationships in the biomedical domain},
+  author       = {
+    Choi, Wonjun and Kim, Baeksoo and Cho, Hyejin and Lee, Doheon and Lee,
+    Hyunju
+  },
+  year         = 2016,
+  journal      = {BMC bioinformatics},
+  publisher    = {Springer},
+  volume       = 17,
+  number       = 1,
+  pages        = {1--15}
+}
+"""
+
+_DATASETNAME = "pcr"
+_DISPLAYNAME = "PCR"
+
+_DESCRIPTION = """
+A corpus for plant / herb and chemical entities and for the relationships \
+between them. The corpus contains 2218 plant and chemical entities and 600 \
+plant-chemical relationships which are drawn from 1109 sentences in 245 PubMed \
+abstracts.
+"""
+
+_HOMEPAGE = "http://210.107.182.73/plantchemcorpus.htm"
+_LICENSE = 'License information unavailable'
+
+_URLS = {_DATASETNAME: "http://210.107.182.73/1109_corpus_units_STformat.tar"}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.EVENT_EXTRACTION]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class PCRDataset(datasets.GeneratorBasedBuilder):
+    """
+    The corpus of plant-chemical relation consists of plants / herbs and
+    chemicals and relations between them.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="pcr_source",
+            version=SOURCE_VERSION,
+            description="PCR source schema",
+            schema="source",
+            subset_id="pcr",
+        ),
+        BigBioConfig(
+            name="pcr_fixed_source",
+            version=SOURCE_VERSION,
+            description="PCR (with fixed offsets) source schema",
+            schema="source",
+            subset_id="pcr_fixed",
+        ),
+        BigBioConfig(
+            name="pcr_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="PCR BigBio schema",
+            schema="bigbio_kb",
+            subset_id="pcr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "pcr_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entities": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "normalized": [
+                                {
+                                    "db_name": datasets.Value("string"),
+                                    "db_id": datasets.Value("string"),
+                                }
+                            ],
+                        }
+                    ],
+                    "events": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            # refers to the text_bound_annotation of the trigger
+                            "trigger": {
+                                "text": datasets.Sequence(datasets.Value("string")),
+                                "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            },
+                            "arguments": [
+                                {
+                                    "role": datasets.Value("string"),
+                                    "ref_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 / "1109 corpus units"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir},
+            )
+        ]
+
+    def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
+        if self.config.schema == "source":
+            for file in data_dir.iterdir():
+                if not str(file).endswith(".txt"):
+                    continue
+
+                example = parsing.parse_brat_file(file)
+                example = parsing.brat_parse_to_bigbio_kb(example)
+                example = self._to_source_example(example)
+
+                # Three documents have incorrect offsets - fix them for fixed_source scheme
+                if self.config.subset_id == "pcr_fixed" and example["document_id"] in [
+                    "463",
+                    "509",
+                    "566",
+                ]:
+                    example = self._fix_example(example)
+
+                yield example["document_id"], example
+
+        elif self.config.schema == "bigbio_kb":
+            for file in data_dir.iterdir():
+                if not str(file).endswith(".txt"):
+                    continue
+
+                example = parsing.parse_brat_file(file)
+                example = parsing.brat_parse_to_bigbio_kb(example)
+
+                document_id = example["document_id"]
+                example["id"] = document_id
+
+                # Three documents have incorrect offsets - fix them for BigBio scheme
+                if document_id in ["463", "509", "566"]:
+                    example = self._fix_example(example)
+
+                yield example["id"], example
+
+    def _to_source_example(self, bigbio_example: Dict) -> Dict:
+        """
+        Converts an example in BigBio-KB scheme to an example according to the source scheme
+        """
+        source_example = bigbio_example.copy()
+        source_example["text"] = bigbio_example["passages"][0]["text"][0]
+
+        source_example.pop("passages", None)
+        source_example.pop("relations", None)
+        source_example.pop("coreferences", None)
+
+        return source_example
+
+    def _fix_example(self, example: Dict) -> Dict:
+        """
+        Fixes by the example by adapting the offsets of the trigger word of the first
+        event. In the official annotation data the end offset is incorrect (for 3 examples).
+        """
+        first_event = example["events"][0]
+        trigger_text = first_event["trigger"]["text"][0]
+        offsets = first_event["trigger"]["offsets"][0]
+
+        real_offsets = [offsets[0], offsets[0] + len(trigger_text)]
+        example["events"][0]["trigger"]["offsets"] = [real_offsets]
+
+        return example