upload hubscripts/bionlp_st_2019_bb_hub.py to hub from bigbio repo

bionlp_st_2019_bb.py

@@ -0,0 +1,594 @@
+# 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.
+
+from pathlib import Path
+from typing import Dict, List
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_DATASETNAME = "bionlp_st_2019_bb"
+_DISPLAYNAME = "BioNLP 2019 BB"
+
+_SOURCE_VIEW_NAME = "source"
+_UNIFIED_VIEW_NAME = "bigbio"
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@inproceedings{bossy-etal-2019-bacteria,
+    title = "Bacteria Biotope at {B}io{NLP} Open Shared Tasks 2019",
+    author = "Bossy, Robert  and
+      Del{\'e}ger, Louise  and
+      Chaix, Estelle  and
+      Ba, Mouhamadou  and
+      N{\'e}dellec, Claire",
+    booktitle = "Proceedings of The 5th Workshop on BioNLP Open Shared Tasks",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-5719",
+    doi = "10.18653/v1/D19-5719",
+    pages = "121--131",
+    abstract = "This paper presents the fourth edition of the Bacteria
+    Biotope task at BioNLP Open Shared Tasks 2019. The task focuses on
+    the extraction of the locations and phenotypes of microorganisms
+    from PubMed abstracts and full-text excerpts, and the characterization
+    of these entities with respect to reference knowledge sources (NCBI
+    taxonomy, OntoBiotope ontology). The task is motivated by the importance
+    of the knowledge on biodiversity for fundamental research and applications
+    in microbiology. The paper describes the different proposed subtasks, the
+    corpus characteristics, and the challenge organization. We also provide an
+    analysis of the results obtained by participants, and inspect the evolution
+    of the results since the last edition in 2016.",
+}
+"""
+
+_DESCRIPTION = """\
+The task focuses on the extraction of the locations and phenotypes of
+microorganisms from PubMed abstracts and full-text excerpts, and the
+characterization of these entities with respect to reference knowledge
+sources (NCBI taxonomy, OntoBiotope ontology). The task is motivated by
+the importance of the knowledge on biodiversity for fundamental research
+and applications in microbiology.
+
+"""
+
+_HOMEPAGE = "https://sites.google.com/view/bb-2019/dataset"
+
+_LICENSE = 'License information unavailable'
+
+_URLs = {
+    "source": {
+        "norm": {
+            "train": "https://drive.google.com/uc?export=download&id=1aXbshxgytZ1Dhbmw7OULPFarPO1FbcM3",
+            "dev": "https://drive.google.com/uc?export=download&id=14jRZWF8VeluEYrV9EybV3LeGm4q5nH6s",
+            "test": "https://drive.google.com/uc?export=download&id=1BPDCFTVMmIlOowYA-DkeNNFjwTfHYPG6",
+        },
+        "norm+ner": {
+            "train": "https://drive.google.com/uc?export=download&id=1yKxBPMej8EYdVeU4QS1xquFfXM76F-2K",
+            "dev": "https://drive.google.com/uc?export=download&id=1Xk7h9bax533QWclO3Ur7aS07OATBF_bG",
+            "test": "https://drive.google.com/uc?export=download&id=1Cb5hQIPS3LIeUL-UWdqyWfKB52xUz9cp",
+        },
+        "rel": {
+            "train": "https://drive.google.com/uc?export=download&id=1gnc-ScNpssC3qrA7cVox4Iei7i96sYqC",
+            "dev": "https://drive.google.com/uc?export=download&id=1wJM9XOfmvIBcX23t9bzQX5fLZwWQJIwS",
+            "test": "https://drive.google.com/uc?export=download&id=1smhKA4LEPK5UJEyBLseq0mBaT9REUevu",
+        },
+        "rel+ner": {
+            "train": "https://drive.google.com/uc?export=download&id=1CPx9NxTPQbygqMtxw3d0hNFajhecqgss",
+            "dev": "https://drive.google.com/uc?export=download&id=1lVyCCuAJ5TmmTDz4S0dISBNiWGR745_7",
+            "test": "https://drive.google.com/uc?export=download&id=1uE8oY5m-7mSA-W-e6vownnAVV97IwHhA",
+        },
+        "kb": {
+            "train": "https://drive.google.com/uc?export=download&id=1Iuce3T_IArXWBbIJ7RXb_STaPnWKQBN-",
+            "dev": "https://drive.google.com/uc?export=download&id=14yON_Tc9dm8esWYDVxL-krw23sgTCcdL",
+            "test": "https://drive.google.com/uc?export=download&id=1wVqI_t9mirGUk71BkwkcKJv0VNGyaHDs",
+        },
+        "kb+ner": {
+            "train": "https://drive.google.com/uc?export=download&id=1WMl9eD4OZXq8zkkmHp3hSEvAqaAVui6L",
+            "dev": "https://drive.google.com/uc?export=download&id=1oOfOfjIfg1XnesXwaKvSDfqgnchuximG",
+            "test": "https://drive.google.com/uc?export=download&id=1_dRbgpGJUBCfF-iN2qOAgOBRvYmE7byW",
+        },
+    },
+    "bigbio_kb": {
+        "kb+ner": {
+            "train": "https://drive.google.com/uc?export=download&id=1WMl9eD4OZXq8zkkmHp3hSEvAqaAVui6L",
+            "dev": "https://drive.google.com/uc?export=download&id=1oOfOfjIfg1XnesXwaKvSDfqgnchuximG",
+            "test": "https://drive.google.com/uc?export=download&id=1_dRbgpGJUBCfF-iN2qOAgOBRvYmE7byW",
+        },
+    },
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.NAMED_ENTITY_DISAMBIGUATION,
+    Tasks.RELATION_EXTRACTION,
+]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class bionlp_st_2019_bb(datasets.GeneratorBasedBuilder):
+    """This dataset is the fourth edition of the Bacteria
+    Biotope task at BioNLP Open Shared Tasks 2019"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bionlp_st_2019_bb_norm_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb entity normalization source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_norm+ner_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb entity recognition and normalization source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_rel_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb relation extraction source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_rel+ner_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb entity recognition and relation extraction source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_kb_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb entity normalization and relation extraction source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_kb+ner_source",
+            version=SOURCE_VERSION,
+            description="bionlp_st_2019_bb entity recognition and normalization and relation extraction source schema",
+            schema="source",
+            subset_id="bionlp_st_2019_bb",
+        ),
+        BigBioConfig(
+            name="bionlp_st_2019_bb_bigbio_kb",
+            version=BIGBIO_VERSION,
+            description="bionlp_st_2019_bb BigBio schema",
+            schema="bigbio_kb",
+            subset_id="bionlp_st_2019_bb",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bionlp_st_2019_bb_kb+ner_source"
+
+    def _info(self):
+        """
+        - `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"),
+                            "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
+                        }
+                    ],
+                },
+            )
+        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: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        version = self.config.name.split("_")[-2]
+        if version == "bigbio":
+            version = "kb+ner"
+        my_urls = _URLs[self.config.schema][version]
+        data_files = {
+            "train": Path(dl_manager.download_and_extract(my_urls["train"]))
+            / f"BioNLP-OST-2019_BB-{version}_train",
+            "dev": Path(dl_manager.download_and_extract(my_urls["dev"]))
+            / f"BioNLP-OST-2019_BB-{version}_dev",
+            "test": Path(dl_manager.download_and_extract(my_urls["test"]))
+            / f"BioNLP-OST-2019_BB-{version}_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 _generate_examples(self, data_files: Path):
+        if self.config.schema == "source":
+            txt_files = list(data_files.glob("*txt"))
+            for guid, txt_file in enumerate(txt_files):
+                example = self.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(
+                    self.parse_brat_file(txt_file)
+                )
+                example["id"] = str(guid)
+                yield guid, example
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+    def parse_brat_file(
+        self,
+        txt_file: Path,
+        annotation_file_suffixes: List[str] = None,
+        parse_notes: bool = False,
+    ) -> Dict:
+        """
+        Parse a brat file into the schema defined below.
+        `txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
+        Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
+        e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
+
+        Will include annotator notes, when `parse_notes == True`.
+
+        brat_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"
+                    }
+                ],
+                ### OPTIONAL: Only included when `parse_notes == True`
+                "notes": [  # # lines in brat
+                    {
+                        "id": datasets.Value("string"),
+                        "type": datasets.Value("string"),
+                        "ref_id": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                    }
+                ],
+            },
+            )
+        """
+
+        example = {}
+        example["document_id"] = txt_file.with_suffix("").name
+        with txt_file.open(encoding="utf-8") as f:
+            if self.config.schema == "bigbio_kb":
+                example["text"] = f.read().replace("\u00A0", " ").replace("\n", " ")
+            else:
+                example["text"] = f.read()
+
+        # If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
+        # for event extraction
+        if annotation_file_suffixes is None:
+            annotation_file_suffixes = [".a1", ".a2", ".ann"]
+
+        if len(annotation_file_suffixes) == 0:
+            raise AssertionError(
+                "At least one suffix for the to-be-read annotation files should be given!"
+            )
+
+        ann_lines = []
+        for suffix in annotation_file_suffixes:
+            annotation_file = txt_file.with_suffix(suffix)
+            if annotation_file.exists():
+                with annotation_file.open(encoding="utf8") as f:
+                    ann_lines.extend(f.readlines())
+
+        example["text_bound_annotations"] = []
+        example["events"] = []
+        example["relations"] = []
+        example["equivalences"] = []
+        example["attributes"] = []
+        example["normalizations"] = []
+
+        if parse_notes:
+            example["notes"] = []
+
+        for line in ann_lines:
+            line = line.strip()
+            if not line:
+                continue
+
+            if line.startswith("T"):  # Text bound
+                ann = {}
+                fields = line.split("\t")
+                ann["id"] = fields[0]
+                ann["type"] = fields[1].split()[0]
+                if ann["type"] in ["Title", "Paragraph"]:
+                    continue
+                ann["offsets"] = []
+                span_str = parsing.remove_prefix(fields[1], (ann["type"] + " "))
+                text = fields[2]
+                for span in span_str.split(";"):
+                    start, end = span.split()
+                    ann["offsets"].append([int(start), int(end)])
+
+                # Heuristically split text of discontiguous entities into chunks
+                ann["text"] = []
+                if len(ann["offsets"]) > 1:
+                    i = 0
+                    for start, end in ann["offsets"]:
+                        chunk_len = end - start
+                        if self.config.schema == "bigbio_kb":
+                            ann["text"].append(
+                                text[i : chunk_len + i].replace("\u00A0", " ")
+                            )
+                        else:
+                            ann["text"].append(text[i : chunk_len + i])
+                        i += chunk_len
+                        while i < len(text) and text[i] == " ":
+                            i += 1
+                else:
+                    if self.config.schema == "bigbio_kb":
+                        ann["text"] = [text.replace("\u00A0", " ")]
+                    else:
+                        ann["text"] = [text]
+
+                example["text_bound_annotations"].append(ann)
+
+            elif line.startswith("E"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+
+                ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
+
+                ann["arguments"] = []
+                for role_ref_id in fields[1].split()[1:]:
+                    argument = {
+                        "role": (role_ref_id.split(":"))[0],
+                        "ref_id": (role_ref_id.split(":"))[1],
+                    }
+                    ann["arguments"].append(argument)
+
+                example["events"].append(ann)
+
+            elif line.startswith("R"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+                ann["type"] = fields[1].split()[0]
+
+                ann["head"] = {
+                    "role": fields[1].split()[1].split(":")[0],
+                    "ref_id": fields[1].split()[1].split(":")[1],
+                }
+                ann["tail"] = {
+                    "role": fields[1].split()[2].split(":")[0],
+                    "ref_id": fields[1].split()[2].split(":")[1],
+                }
+
+                example["relations"].append(ann)
+
+            # '*' seems to be the legacy way to mark equivalences,
+            # but I couldn't find any info on the current way
+            # this might have to be adapted dependent on the brat version
+            # of the annotation
+            elif line.startswith("*"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+                ann["ref_ids"] = fields[1].split()[1:]
+
+                example["equivalences"].append(ann)
+
+            elif line.startswith("A") or line.startswith("M"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+
+                info = fields[1].split()
+                ann["type"] = info[0]
+                ann["ref_id"] = info[1]
+
+                if len(info) > 2:
+                    ann["value"] = info[2]
+                else:
+                    ann["value"] = ""
+
+                example["attributes"].append(ann)
+
+            elif line.startswith("N"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+
+                info = fields[1].split()
+
+                ann["ref_id"] = info[1].split(":")[-1]
+                ann["resource_name"] = info[0]
+                ann["cuid"] = "".join(info[2].split(":")[1:])
+                example["normalizations"].append(ann)
+
+            elif parse_notes and line.startswith("#"):
+                ann = {}
+                fields = line.split("\t")
+
+                ann["id"] = fields[0]
+                ann["text"] = fields[2]
+
+                info = fields[1].split()
+
+                ann["type"] = info[0]
+                ann["ref_id"] = info[1]
+                example["notes"].append(ann)
+        return example