upload hubscripts/mlee_hub.py to hub from bigbio repo

mlee.py

@@ -0,0 +1,279 @@
+# coding=utf-8
+# Copyright 2020 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.
+
+"""
+MLEE is an event extraction corpus consisting of manually annotated abstracts of papers
+on angiogenesis. It contains annotations for entities, relations, events and coreferences
+The annotations span molecular, cellular, tissue, and organ-level processes.
+"""
+from pathlib import Path
+from typing import Dict, List
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_SOURCE_VIEW_NAME = "source"
+_UNIFIED_VIEW_NAME = "bigbio"
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{pyysalo2012event,
+  title={Event extraction across multiple levels of biological organization},
+  author={Pyysalo, Sampo and Ohta, Tomoko and Miwa, Makoto and Cho, Han-Cheol and Tsujii, Jun'ichi and Ananiadou, Sophia},
+  journal={Bioinformatics},
+  volume={28},
+  number={18},
+  pages={i575--i581},
+  year={2012},
+  publisher={Oxford University Press}
+}
+"""
+
+_DESCRIPTION = """\
+MLEE is an event extraction corpus consisting of manually annotated abstracts of papers
+on angiogenesis. It contains annotations for entities, relations, events and coreferences
+The annotations span molecular, cellular, tissue, and organ-level processes.
+"""
+
+_DATASETNAME = "mlee"
+_DISPLAYNAME = "MLEE"
+
+_HOMEPAGE = "http://www.nactem.ac.uk/MLEE/"
+
+_LICENSE = 'Creative Commons Attribution Non Commercial Share Alike 3.0 Unported'
+_URLs = {
+    "source": "http://www.nactem.ac.uk/MLEE/MLEE-1.0.2-rev1.tar.gz",
+    "bigbio_kb": "http://www.nactem.ac.uk/MLEE/MLEE-1.0.2-rev1.tar.gz",
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.EVENT_EXTRACTION,
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.RELATION_EXTRACTION,
+    Tasks.COREFERENCE_RESOLUTION,
+]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MLEE(datasets.GeneratorBasedBuilder):
+    """Write a short docstring documenting what this dataset is"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="mlee_source",
+            version=SOURCE_VERSION,
+            description="MLEE source schema",
+            schema="source",
+            subset_id="mlee",
+        ),
+        BigBioConfig(
+            name="mlee_bigbio_kb",
+            version=SOURCE_VERSION,
+            description="MLEE BigBio schema",
+            schema="bigbio_kb",
+            subset_id="mlee",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mlee_source"
+
+    _ROLE_MAPPING = {
+        "Theme2": "Theme",
+        "Instrument2": "Instrument",
+        "Participant2": "Participant",
+        "Participant3": "Participant",
+        "Participant4": "Participant",
+    }
+
+    def _info(self):
+        """
+        Provide information about MLEE:
+        - `features` defines the schema of the parsed data set. The schema depends on the
+        chosen `config`: If it is `_SOURCE_VIEW_NAME` the schema is the schema of the
+        original data. If `config` is `_UNIFIED_VIEW_NAME`, then the schema is the
+        canonical KB-task schema defined in `biomedical/schemas/kb.py`.
+
+        """
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "text_bound_annotations": [  # T line in brat, e.g. type or event trigger
+                        {
+                            "offsets": datasets.Sequence([datasets.Value("int32")]),
+                            "text": datasets.Sequence(datasets.Value("string")),
+                            "type": datasets.Value("string"),
+                            "id": datasets.Value("string"),
+                        }
+                    ],
+                    "events": [  # E line in brat
+                        {
+                            "trigger": datasets.Value(
+                                "string"
+                            ),  # refers to the text_bound_annotation of the trigger,
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arguments": datasets.Sequence(
+                                {
+                                    "role": datasets.Value("string"),
+                                    "ref_id": datasets.Value("string"),
+                                }
+                            ),
+                        }
+                    ],
+                    "relations": [  # R line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "head": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "tail": {
+                                "ref_id": datasets.Value("string"),
+                                "role": datasets.Value("string"),
+                            },
+                            "type": datasets.Value("string"),
+                        }
+                    ],
+                    "equivalences": [  # Equiv line in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "ref_ids": datasets.Sequence(datasets.Value("string")),
+                        }
+                    ],
+                    "attributes": [  # M or A lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "value": datasets.Value("string"),
+                        }
+                    ],
+                    "normalizations": [  # N lines in brat
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                            "resource_name": datasets.Value(
+                                "string"
+                            ),  # Name of the resource, e.g. "Wikipedia"
+                            "cuid": datasets.Value(
+                                "string"
+                            ),  # ID in the resource, e.g. 534366
+                            "text": datasets.Value(
+                                "string"
+                            ),  # Human readable description/name of the entity, e.g. "Barack Obama"
+                        }
+                    ],
+                },
+            )
+        elif self.config.schema == "bigbio_kb":
+            features = kb_features
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=features,
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # This is not applicable for MLEE.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=str(_LICENSE),
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        """
+        Create the three splits provided by MLEE: train, validation and test.
+
+        Each split is created by instantiating a `datasets.SplitGenerator`, which will
+        call `this._generate_examples` with the keyword arguments in `gen_kwargs`.
+        """
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = Path(dl_manager.download_and_extract(my_urls))
+        data_files = {
+            "train": data_dir
+            / "MLEE-1.0.2-rev1"
+            / "standoff"
+            / "development"
+            / "train",
+            "dev": data_dir / "MLEE-1.0.2-rev1" / "standoff" / "development" / "test",
+            "test": data_dir / "MLEE-1.0.2-rev1" / "standoff" / "test" / "test",
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_files": data_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_files": data_files["dev"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_files": data_files["test"]},
+            ),
+        ]
+
+    def _standardize_arguments_roles(self, kb_example: Dict) -> Dict:
+
+        for event in kb_example["events"]:
+            for argument in event["arguments"]:
+                role = argument["role"]
+                argument["role"] = self._ROLE_MAPPING.get(role, role)
+
+        return kb_example
+
+    def _generate_examples(self, data_files: Path):
+        """
+        Yield one `(guid, example)` pair per abstract in MLEE.
+        The contents of `example` will depend on the chosen configuration.
+        """
+        if self.config.schema == "source":
+            txt_files = list(data_files.glob("*txt"))
+            for guid, txt_file in enumerate(txt_files):
+                example = parsing.parse_brat_file(txt_file)
+                example["id"] = str(guid)
+                yield guid, example
+        elif self.config.schema == "bigbio_kb":
+            txt_files = list(data_files.glob("*txt"))
+            for guid, txt_file in enumerate(txt_files):
+                example = parsing.brat_parse_to_bigbio_kb(
+                    parsing.parse_brat_file(txt_file)
+                )
+                example = self._standardize_arguments_roles(example)
+                example["id"] = str(guid)
+                yield guid, example
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")