# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and Gully Burns. # # 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. """ Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: (A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); - Clinical Characteristics, Disease Pathology, and Diagnosis - Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; (B) the effects of the disease on patient organs, tissues, or cells; (C) the results of clinical tests that reveal pathology (including biomarkers); (D) research that use this information to figure out a diagnosis. - Therapeutics in the clinic - Text describing how treatments work in the clinic (but not in a clinical trial). - Disease mechanism - Text that describes either (A) mechanistic involvement of specific genes in disease (deletions, gain of function, etc); (B) how molecular signalling or metabolism binding, activating, phosphorylation, concentration increase, etc.) are involved in the mechanism of a disease; or (C) the physiological mechanism of disease at the level of tissues, organs, and body systems. - Patient-Based Therapeutics - Text describing (A) Clinical trials (studies of therapeutic measures being used on patients in a clinical trial); (B) Post Marketing Drug Surveillance (effects of a drug after approval in the general population or as part of ‘standard healthcare’); (C) Drug repurposing (how a drug that has been approved for one use is being applied to a new disease). (B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); - -1 - the paper is not a primary experimental study in rare disease - 0 - the study does not directly investigate quality of life - 1 - the study investigates qol but not as its primary contribution - 2 - the study's primary contribution centers on quality of life measures (C) identifies if a paper is a natural history study (~10k papers). ` - -1 - the paper is not a primary experimental study in rare disease - 0 - the study is not directly investigating the natural history of a disease - 1 - the study includes some elements a natural history but not as its primary contribution - 2 - the study's primary contribution centers on observing the time course of a rare disease These classifications are particularly relevant in rare disease research, a field that is generally understudied. """ import os from typing import List, Tuple, Dict import datasets import pandas as pd from pathlib import Path from .bigbiohub import text_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _LOCAL = False _CITATION = """\ @article{, author = {}, title = {}, journal = {}, volume = {}, year = {}, url = {}, doi = {}, biburl = {}, bibsource = {} } """ _DATASETNAME = "czi_drsm" _DESCRIPTION = """\ Research Article document classification dataset based on aspects of disease research. Currently, the dataset consists of three subsets: (A) classifies title/abstracts of papers into most popular subtypes of clinical, basic, and translational papers (~20k papers); - Clinical Characteristics, Disease Pathology, and Diagnosis - Text that describes (A) symptoms, signs, or ‘phenotype’ of a disease; (B) the effects of the disease on patient organs, tissues, or cells; (C) the results of clinical tests that reveal pathology (including biomarkers); (D) research that use this information to figure out a diagnosis. - Therapeutics in the clinic - Text describing how treatments work in the clinic (but not in a clinical trial). - Disease mechanism - Text that describes either (A) mechanistic involvement of specific genes in disease (deletions, gain of function, etc); (B) how molecular signalling or metabolism binding, activating, phosphorylation, concentration increase, etc.) are involved in the mechanism of a disease; or (C) the physiological mechanism of disease at the level of tissues, organs, and body systems. - Patient-Based Therapeutics - Text describing (A) Clinical trials (studies of therapeutic measures being used on patients in a clinical trial); (B) Post Marketing Drug Surveillance (effects of a drug after approval in the general population or as part of ‘standard healthcare’); (C) Drug repurposing (how a drug that has been approved for one use is being applied to a new disease). (B) identifies whether a title/abstract of a paper describes substantive research into Quality of Life (~10k papers); - -1 - the paper is not a primary experimental study in rare disease - 0 - the study does not directly investigate quality of life - 1 - the study investigates qol but not as its primary contribution - 2 - the study's primary contribution centers on quality of life measures (C) identifies if a paper is a natural history study (~10k papers). ` - -1 - the paper is not a primary experimental study in rare disease - 0 - the study is not directly investigating the natural history of a disease - 1 - the study includes some elements a natural history but not as its primary contribution - 2 - the study's primary contribution centers on observing the time course of a rare disease These classifications are particularly relevant in rare disease research, a field that is generally understudied. """ _HOMEPAGE = "https://github.com/chanzuckerberg/DRSM-corpus/" _LICENSE = 'CC0_1p0' _LANGUAGES = ['English'] _PUBMED = False _LOCAL = False _DISPLAYNAME = "DRSM Corpus" # For publicly available datasets you will most likely end up passing these URLs to dl_manager in _split_generators. # In most cases the URLs will be the same for the source and bigbio config. # However, if you need to access different files for each config you can have multiple entries in this dict. # This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method) _URLS = { 'base': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v1/drsm_corpus_v1.tsv", 'qol': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v2/qol_all_2022_12_15.tsv", 'nhs': "https://raw.githubusercontent.com/chanzuckerberg/DRSM-corpus/main/v2/nhs_all_2023_03_31.tsv" } _SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" _CLASS_NAMES_BASE = [ "clinical characteristics or disease pathology", "therapeutics in the clinic", "disease mechanism", "patient-based therapeutics", "other", "irrelevant" ] _CLASS_NAMES_QOL = [ "-1 - the paper is not a primary experimental study in rare disease", "0 - the study does not directly investigate quality of life", "1 - the study investigates qol but not as its primary contribution", "2 - the study's primary contribution centers on quality of life measures" ] _CLASS_NAMES_NHS = [ "-1 - the paper is not a primary experimental study in rare disease", "0 - the study is not directly investigating the natural history of a disease", "1 - the study includes some elements a natural history but not as its primary contribution", "2 - the study's primary contribution centers on observing the time course of a rare disease" ] class DRSMBaseDataset(datasets.GeneratorBasedBuilder): """DRSM Document Classification Datasets.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) # You will be able to load the "source" or "bigbio" configurations with #ds_source = datasets.load_dataset('drsm_source_dataset', name='source') #ds_bigbio = datasets.load_dataset('drsm_bigbio_dataset', name='bigbio') # For local datasets you can make use of the `data_dir` and `data_files` kwargs # https://huggingface.co/docs/datasets/add_dataset.html#downloading-data-files-and-organizing-splits # ds_source = datasets.load_dataset('my_dataset', name='source', data_dir="/path/to/data/files") # ds_bigbio = datasets.load_dataset('my_dataset', name='bigbio', data_dir="/path/to/data/files") # TODO: For each dataset, implement Config for Source and BigBio; # If dataset contains more than one subset (see examples/bioasq.py) implement for EACH of them. # Each of them should contain: # - name: should be unique for each dataset config eg. bioasq10b_(source|bigbio)_[bigbio_schema_name] # - version: option = (SOURCE_VERSION|BIGBIO_VERSION) # - description: one line description for the dataset # - schema: options = (source|bigbio_[bigbio_schema_name]) # - subset_id: subset id is the canonical name for the dataset (eg. bioasq10b) # where [bigbio_schema_name] = () BUILDER_CONFIGS = [ BigBioConfig( name="czi_drsm_base_source", version=SOURCE_VERSION, description="czi_drsm base source schema", schema="base_source", subset_id="czi_drsm_base", ), BigBioConfig( name="czi_drsm_bigbio_base_text", version=BIGBIO_VERSION, description="czi_drsm base BigBio schema", schema="bigbio_text", subset_id="czi_drsm_base", ), BigBioConfig( name="czi_drsm_qol_source", version=SOURCE_VERSION, description="czi_drsm source schema for Quality of Life studies", schema="qol_source", subset_id="czi_drsm_qol", ), BigBioConfig( name="czi_drsm_bigbio_qol_text", version=BIGBIO_VERSION, description="czi_drsm BigBio schema for Quality of Life studies", schema="bigbio_text", subset_id="czi_drsm_qol", ), BigBioConfig( name="czi_drsm_nhs_source", version=SOURCE_VERSION, description="czi_drsm source schema for Natural History Studies", schema="nhs_source", subset_id="czi_drsm_nhs", ), BigBioConfig( name="czi_drsm_bigbio_nhs_text", version=BIGBIO_VERSION, description="czi_drsm BigBio schema for Natural History Studies", schema="bigbio_text", subset_id="czi_drsm_nhs", ), ] DEFAULT_CONFIG_NAME = "czi_drsm_bigbio_base_text" def _info(self) -> datasets.DatasetInfo: # Create the source schema; this schema will keep all keys/information/labels as close to the original dataset as possible. # You can arbitrarily nest lists and dictionaries. # For iterables, use lists over tuples or `datasets.Sequence` if self.config.schema == "base_source": features = datasets.Features( { "document_id": datasets.Value("string"), "labeling_state": datasets.Value("string"), "explanation": datasets.Value("string"), "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_BASE)], "agreement": [datasets.Value("string")], "title": [datasets.Value("string")], "abstract": [datasets.Value("string")], } ) elif self.config.schema == "qol_source": features = datasets.Features( { "document_id": datasets.Value("string"), "labeling_state": datasets.Value("string"), "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_QOL)], "explanation": datasets.Value("string"), "agreement": [datasets.Value("string")], "title": [datasets.Value("string")], "abstract": [datasets.Value("string")] } ) elif self.config.schema == "nhs_source": features = datasets.Features( { "document_id": datasets.Value("string"), "labeling_state": datasets.Value("string"), "correct_label": [datasets.ClassLabel(names=_CLASS_NAMES_NHS)], "explanation": datasets.Value("string"), "agreement": [datasets.Value("string")], "title": [datasets.Value("string")], "abstract": [datasets.Value("string")], } ) # For example bigbio_kb, bigbio_t2t elif self.config.schema == "bigbio_text": features = text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" if 'base' in self.config.name: url = _URLS['base'] elif 'qol' in self.config.name: url = _URLS['qol'] elif 'nhs' in self.config.name: url = _URLS['nhs'] else: raise ValueError("Invalid config name: {}".format(self.config.name)) data_file = dl_manager.download_and_extract(url) df = pd.read_csv(data_file, sep="\t", encoding="utf-8").fillna('') # load tsv file into huggingface dataset ds = datasets.Dataset.from_pandas(df) # generate train_test split ds_dict = ds.train_test_split(test_size=0.2, seed=42) ds_dict2 = ds_dict['test'].train_test_split(test_size=0.5, seed=42) # dump train, val, test to disk data_dir = Path(data_file).parent ds_dict['train'].to_csv(data_dir / "train.tsv", sep="\t", index=False) ds_dict2['train'].to_csv(data_dir / "validation.tsv", sep="\t", index=False) ds_dict2['test'].to_csv(data_dir / "test.tsv", sep="\t", index=False) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": data_dir / "train.tsv", "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": data_dir / "validation.tsv", "split": "validation", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": data_dir / "test.tsv", "split": "test", }, ) ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" df = pd.read_csv(filepath, sep="\t", encoding="utf-8").fillna('') print(len(df)) for id_, l in df.iterrows(): if self.config.subset_id == "czi_drsm_base": doc_id = l[0] labeling_state = l[1] correct_label = l[2] agreement = l[3] explanation = l[4] title = l[5] abstract = l[6] elif self.config.subset_id == "czi_drsm_qol": doc_id = l[0] labeling_state = l[1] correct_label = l[2][1:-1] explanation = l[3] agreement = l[4] title = l[5] abstract = l[6] elif self.config.subset_id == "czi_drsm_nhs": doc_id = l[0] labeling_state = l[1] correct_label = l[2][1:-1] explanation = '' agreement = l[3] title = l[4] abstract = l[5] if "_source" in self.config.schema: yield id_, { "document_id": doc_id, "labeling_state": labeling_state, "explanation": explanation, "correct_label": [correct_label], "agreement": str(agreement), "title": title, "abstract": abstract } elif self.config.schema == "bigbio_text": yield id_, { "id": id_, "document_id": doc_id, "text": title + " " + abstract, "labels": [correct_label] } # This template is based on the following template from the datasets package: # https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py