czi_drsm.py

# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
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