upload hubscripts/spl_adr_200db_hub.py to hub from bigbio repo

spl_adr_200db.py

@@ -0,0 +1,405 @@
+# 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.
+
+"""
+Dataset containing standardised information about known adverse reactions for 200
+FDA-approved drugs using information from the respective Structured Product Labels (SPLs).
+This data resulted from a partnership between the United States Food and Drug Administration
+(FDA) and the National Library of Medicine.
+
+Structured Product Labels (SPLs) are the documents FDA uses to exchange information
+about drugs and other products. For this dataset, SPLs were manually annotated for
+adverse reactions at the mention level to facilitate development and evaluation of
+text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised
+to the Unified Medical Language System (UMLS) and to the Medical Dictionary for
+Regulatory Activities (MedDRA).
+
+These data were used for the adverse event challenge at TAC 2017 (Text Analysis Conference)
+in four different tasks:
+* Task 1: Extract AdverseReactions and related mentions (Severity, Factor, DrugClass,
+Negation, Animal). This is similar to many NLP Named Entity Recognition (NER) evaluations.
+* Task 2: Identify the relations between AdverseReactions and related mentions (i.e.,
+Negated, Hypothetical, and Effect). This is similar to many NLP relation
+identification evaluations.
+* Task 3: Identify the positive AdverseReaction mention names in the labels.
+For the purposes of this task, positive will be defined as the caseless strings
+of all the AdverseReactions that have not been negated and are not related by
+a Hypothetical relation to a DrugClass or Animal. Note that this means Factors
+related via a Hypothetical relation are considered positive (e.g., "[unknown risk]
+Factor of [stroke]AdverseReaction") for the purposes of this task. The result of
+this task will be a list of unique strings corresponding to the positive ADRs
+as they were written in the label.
+* Task 4: Provide MedDRA PT(s) and LLT(s) for each positive AdverseReaction (occasionally,
+two or more PTs are necessary to fully describe the reaction). For participants
+approaching the tasks sequentially, this can be viewed as normalization of the terms
+extracted in Task 3 to MedDRA LLTs/PTs. Because MedDRA is not publicly available,
+and contains several versions, a standard version of MedDRA v18.1 will be provided
+to the participants. Other resources such as the UMLS Terminology Services may be
+used to aid with the normalization process.
+
+For more information regarding the challenge at TAC 2017, please visit:
+https://bionlp.nlm.nih.gov/tac2017adversereactions/
+
+"""
+
+import xml.etree.ElementTree as ET
+from collections import defaultdict
+from itertools import accumulate
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import kb_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@article{demner2018dataset,
+  author    = {Demner-Fushman, Dina and Shooshan, Sonya and Rodriguez, Laritza and Aronson,
+               Alan and Lang, Francois and Rogers, Willie and Roberts, Kirk and Tonning, Joseph},
+  title     = {A dataset of 200 structured product labels annotated for adverse drug reactions},
+  journal   = {Scientific Data},
+  volume    = {5},
+  year      = {2018},
+  month     = {01},
+  pages     = {180001},
+  url       = {
+    https://www.researchgate.net/publication/322810855_A_dataset_of_200_structured_product_labels_annotated_for_adverse_drug_reactions
+  },
+  doi       = {10.1038/sdata.2018.1}
+}
+"""
+
+_DATASETNAME = "spl_adr_200db"
+_DISPLAYNAME = "SPL ADR"
+
+_DESCRIPTION = """\
+The United States Food and Drug Administration (FDA) partnered with the National Library
+of Medicine to create a pilot dataset containing standardised information about known
+adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs),
+the documents FDA uses to exchange information about drugs and other products, were
+manually annotated for adverse reactions at the mention level to facilitate development
+and evaluation of text mining tools for extraction of ADRs from all SPLs.  The ADRs were
+then normalised to the Unified Medical Language System (UMLS) and to the Medical
+Dictionary for Regulatory Activities (MedDRA).
+"""
+
+_HOMEPAGE = "https://bionlp.nlm.nih.gov/tac2017adversereactions/"
+
+# NOTE: Source: https://osf.io/6h9q4/
+_LICENSE = 'Creative Commons Zero v1.0 Universal'
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://bionlp.nlm.nih.gov/tac2017adversereactions/train_xml.tar.gz",
+        "unannotated": "https://bionlp.nlm.nih.gov/tac2017adversereactions/unannotated_xml.tar.gz",
+    }
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.NAMED_ENTITY_RECOGNITION,
+    Tasks.NAMED_ENTITY_DISAMBIGUATION,
+    Tasks.RELATION_EXTRACTION,
+]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class SplAdr200DBDataset(datasets.GeneratorBasedBuilder):
+    """
+    The United States Food and Drug Administration (FDA) partnered with the National Library
+    of Medicine to create a pilot dataset containing standardised information about known
+    adverse reactions for 200 FDA-approved drugs.
+
+    These data were used in the adverse event challenge at TAC 2017 (Text Analysis Conference).
+    For more information on the tasks, see: https://bionlp.nlm.nih.gov/tac2017adversereactions/
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = []
+
+    for subset_name in _URLS[_DATASETNAME]:
+        BUILDER_CONFIGS.extend(
+            [
+                BigBioConfig(
+                    name=f"spl_adr_200db_{subset_name}_source",
+                    version=SOURCE_VERSION,
+                    description=f"SPL ADR 200db source {subset_name} schema",
+                    schema="source",
+                    subset_id=f"spl_adr_200db_{subset_name}",
+                ),
+                BigBioConfig(
+                    name=f"spl_adr_200db_{subset_name}_bigbio_kb",
+                    version=BIGBIO_VERSION,
+                    description=f"SPL ADR 200db BigBio {subset_name} schema",
+                    schema="bigbio_kb",
+                    subset_id=f"spl_adr_200db_{subset_name}",
+                ),
+            ]
+        )
+
+    DEFAULT_CONFIG_NAME = "spl_adr_200db_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            unannotated_features = {
+                "drug_name": datasets.Value("string"),
+                "text": [datasets.Value("string")],
+                "sections": [
+                    {
+                        "id": datasets.Value("string"),
+                        "name": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                    }
+                ],
+            }
+            features = datasets.Features(
+                {
+                    **unannotated_features,
+                    "mentions": [
+                        {
+                            "id": datasets.Value("string"),
+                            "section": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "start": datasets.Value("string"),
+                            "len": datasets.Value("string"),
+                            "str": datasets.Value("string"),
+                        }
+                    ],
+                    "relations": [
+                        {
+                            "id": datasets.Value("string"),
+                            "type": datasets.Value("string"),
+                            "arg1": datasets.Value("string"),
+                            "arg2": datasets.Value("string"),
+                        }
+                    ],
+                    "reactions": [
+                        {
+                            "id": datasets.Value("string"),
+                            "str": datasets.Value("string"),
+                            "normalizations": [
+                                {
+                                    "id": datasets.Value("string"),
+                                    "meddra_pt": datasets.Value("string"),
+                                    "meddra_pt_id": datasets.Value("string"),
+                                    "meddra_llt": datasets.Value("string"),
+                                    "meddra_llt_id": datasets.Value("string"),
+                                    "flag": 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."""
+        *_, subset_name = self.config.subset_id.split("_")
+
+        urls = _URLS[_DATASETNAME][subset_name]
+
+        data_dir = dl_manager.download_and_extract(urls)
+
+        data_files = (Path(data_dir) / f"{subset_name}_xml").glob("*.xml")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": tuple(data_files),
+                },
+            ),
+        ]
+
+    def _source_features_from_xml(self, element_tree):
+        root = element_tree.getroot()
+        drug_name = root.attrib["drug"]
+
+        sections = root.findall(".//Text/Section")
+        relations = root.findall(".//Relations/Relation")
+        reactions = [
+            {
+                "id": reaction.attrib["id"],
+                "str": reaction.attrib["str"],
+                "normalizations": [
+                    {
+                        # NOTE: Default features to `None` as not all of them
+                        # will be present in all reactions.
+                        "meddra_pt": None,
+                        "meddra_pt_id": None,
+                        "meddra_llt": None,
+                        "meddra_llt_id": None,
+                        "flag": None,
+                        **normalization.attrib,
+                    }
+                    for normalization in reaction.findall("Normalization")
+                ],
+            }
+            for reaction in root.findall(".//Reactions/Reaction")
+        ]
+
+        mentions = root.findall(".//Mentions/Mention")
+        return {
+            "drug_name": drug_name,
+            "text": [section.text for section in sections],
+            "mentions": [mention.attrib for mention in mentions],
+            "relations": [relation.attrib for relation in relations],
+            "reactions": reactions,
+            "sections": [
+                {**section.attrib, "text": section.text} for section in sections
+            ],
+        }
+
+    def _bigbio_kb_features_from_xml(self, element_tree):
+        source_features = self._source_features_from_xml(
+            element_tree=element_tree,
+        )
+        entity_normalizations = defaultdict(list)
+
+        for reaction in source_features["reactions"]:
+            entity_name = reaction["str"]
+            for normalization in reaction["normalizations"]:
+
+                # commenting this out for now
+                # if there is no db_name then its not a useful normalization
+                # if normalization["meddra_pt_id"]:
+                #    entity_normalizations[entity_name].append(
+                #        {"db_name": None, "db_id": f"pt_{normalization['meddra_pt_id']}"}
+                #    )
+
+                if normalization["meddra_llt_id"]:
+                    entity_normalizations[entity_name].append(
+                        {
+                            "db_name": "MedDRA v18.1",
+                            "db_id": f"llt_{normalization['meddra_llt_id']}",
+                        }
+                    )
+
+        section_lengths = list(
+            accumulate(len(section["text"]) for section in source_features["sections"])
+        )
+
+        section_offsets = [
+            (start + index, end + index)
+            for index, (start, end) in enumerate(
+                zip([0] + section_lengths[:-1], section_lengths)
+            )
+        ]
+
+        section_start_offset_map = {
+            f"S{section_index}": offsets[0]
+            for section_index, offsets in enumerate(section_offsets, 1)
+        }
+
+        entities = []
+
+        for mention in source_features["mentions"]:
+            start_points = [
+                int(start_point) + section_start_offset_map[mention["section"]]
+                for start_point in mention["start"].split(",")
+            ]
+
+            lens = [int(len_) for len_ in mention["len"].split(",")]
+
+            offsets = [
+                (start_point, start_point + len_)
+                for start_point, len_ in zip(start_points, lens)
+            ]
+
+            text = " ".join(section["text"] for section in source_features["sections"])
+
+            entity_strings = [
+                text[start_point : start_point + len_]
+                for start_point, len_ in zip(start_points, lens)
+            ]
+
+            entities.append(
+                {
+                    "id": f"{source_features['drug_name']}_entity_{mention['id']}",
+                    "type": mention["type"],
+                    "text": entity_strings,
+                    "offsets": offsets,
+                    "normalized": entity_normalizations[mention["str"]],
+                }
+            )
+
+        return {
+            "document_id": source_features["drug_name"],
+            "passages": [
+                {
+                    "id": f"{source_features['drug_name']}_section_{section['id']}",
+                    "type": section["name"],
+                    "text": [section["text"]],
+                    "offsets": [offsets],
+                }
+                for section, offsets in zip(
+                    source_features["sections"], section_offsets
+                )
+            ],
+            "entities": entities,
+            "relations": [
+                {
+                    "id": f"{source_features['drug_name']}_relation_{relation['id']}",
+                    "type": relation["type"],
+                    "arg1_id": relation["arg1"],
+                    "arg2_id": relation["arg2"],
+                    "normalized": [],
+                }
+                for relation in source_features["relations"]
+            ],
+            "events": [],
+            "coreferences": [],
+        }
+
+    def _generate_examples(self, filepaths: Tuple[Path]) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        for file_index, drug_filename in enumerate(filepaths):
+            element_tree = ET.parse(drug_filename)
+
+            if self.config.schema == "source":
+                features = self._source_features_from_xml(
+                    element_tree=element_tree,
+                )
+            elif self.config.schema == "bigbio_kb":
+                features = self._bigbio_kb_features_from_xml(
+                    element_tree=element_tree,
+                )
+                features["id"] = file_index
+            else:
+                raise ValueError(
+                    f"Unsupported schema '{self.config.schema}' requested for "
+                    f"dataset with name '{_DATASETNAME}'."
+                )
+
+            yield file_index, features