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Closes #716 #722

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Closes #716 #722

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70c3773
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shamikbose Jul 3, 2022
587cda4
Changes for building abstract
shamikbose Jul 5, 2022
4c7b813
Passes all tests
shamikbose Jul 5, 2022
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139 changes: 64 additions & 75 deletions bigbio/biodatasets/thomas2011/thomas2011.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,15 +43,16 @@
from pathlib import Path
from shutil import rmtree
from typing import Dict, List, Tuple

import xml.etree.ElementTree as ET
import datasets
import pandas as pd
import requests

from bigbio.utils import schemas
from bigbio.utils.configs import BigBioConfig
from bigbio.utils.constants import Lang, Tasks
from bigbio.utils.license import CustomLicense

import time

_LANGUAGES = [Lang.EN]
_PUBMED = True
Expand Down Expand Up @@ -98,8 +99,7 @@
# this is a backup url in case the official one will stop working
# _URLS = ["http://github.com/rockt/SETH/zipball/master/"]
_URLS = {
"source": "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/normalization-variation-corpus.gz",
"bigbio_kb": "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/normalization-variation-corpus.gz",
_DATASETNAME: "https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/normalization-variation-corpus.gz",
}

_SUPPORTED_TASKS = [
Expand All @@ -117,52 +117,27 @@ class Thomas2011Dataset(datasets.GeneratorBasedBuilder):
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('my_dataset', name='source')
# ds_bigbio = datasets.load_dataset('my_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] = (kb, pairs, qa, text, t2t, entailment)

BUILDER_CONFIGS = [
BigBioConfig(
name="thomas2011_source",
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description="Thomas et al 2011 source schema",
schema="source",
subset_id="thomas2011",
),
BigBioConfig(
name="thomas2011_bigbio_kb",
name=f"{_DATASETNAME}_bigbio_kb",
version=BIGBIO_VERSION,
description="Thomas et al 2011 BigBio schema",
schema="bigbio_kb",
subset_id="thomas2011",
),
]

DEFAULT_CONFIG_NAME = "thomas2011_source"
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

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.
# Much of this design is copied from biodatasets/verspoor_2013/verspoor_2013.py

# You can arbitrarily nest lists and dictionaries.
# For iterables, use lists over tuples or `datasets.Sequence`

if self.config.schema == "source":
features = datasets.Features(
{
Expand All @@ -188,54 +163,53 @@ def _info(self) -> datasets.DatasetInfo:
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""

# Download gets entire git repo containing unused data from other datasets
# repo_dir = Path(dl_manager.download_and_extract(_URLS[0]))
# data_dir = repo_dir / "data"
# data_dir.mkdir(exist_ok=True)

# Find the relevant files from Verspor2013 and move them to a new directory
# thomas2011_files = repo_dir.glob("*/*/*thomas2011/**/*")
# for file in thomas2011_files:
# if file.is_file() and "README" not in str(file):
# file.rename(data_dir / file.name)

# Delete all unused files and directories from the original download
# for x in repo_dir.glob("[!data]*"):
# if x.is_file():
# x.unlink()
# elif x.is_dir():
# rmtree(x)

data_dir = dl_manager.download_and_extract(_URLS[self.config.schema])
data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
name=datasets.Split.TRAIN,
# Whatever you put in gen_kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "annotations.txt"),
"split": "test",
},
)
]

# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`

# TODO: change the args of this function to match the keys in `gen_kwargs`. You may add any necessary kwargs.
def _generate_examples(self, filepath: str, split: str) -> Tuple[int, Dict]:
def get_clean_pubmed_abstract(self, id):
url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?"
params = {
"db": "pubmed",
"id": id,
"retmode": "xml",
"rettype": "medline",
}
res = requests.get(url, params=params)
tree = ET.XML(res.text)
article = tree.find("PubmedArticle").find("MedlineCitation").find("Article")
article_title = article.find("ArticleTitle").text
abstract_parts = [f"{article_title}"]
article_abstract = article.find("Abstract").findall("AbstractText")
for abstract_part in article_abstract:
label = abstract_part.attrib.get("Label", "")
if label:
abstract_parts.append(f"{label}: {abstract_part.text}")
else:
abstract_parts.append(abstract_part.text)
return article_title, " ".join(abstract_parts)

def _generate_examples(self, filepath: str) -> Tuple[int, Dict]:

"""Yields examples as (key, example) tuples."""
if split == "test":
data_ann = []
with open(filepath, encoding="utf-8") as ann_tsv_file:
csv_reader_code = csv.reader(
ann_tsv_file,
quotechar="'",
delimiter="\t",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
for id_, row in enumerate(csv_reader_code):
data_ann.append(row)
data_ann = []
with open(filepath, encoding="utf-8") as ann_tsv_file:
csv_reader_code = csv.reader(
ann_tsv_file,
quotechar="'",
delimiter="\t",
quoting=csv.QUOTE_ALL,
skipinitialspace=True,
)
for id_, row in enumerate(csv_reader_code):
data_ann.append(row)

if self.config.schema == "source":
for id_, row in enumerate(data_ann):
Expand All @@ -259,33 +233,48 @@ def _generate_examples(self, filepath: str, split: str) -> Tuple[int, Dict]:
]
df = pd.DataFrame(data_ann, columns=cols)
uid = 0
curr_count = 0
for id_ in df.doc_id.unique():
curr_count += 1
if curr_count == 3:
# The PubMed API limits 3 requests per second without an API key
time.sleep(0.5)
curr_count = 0
elist = []
article_title, abstract_text = self.get_clean_pubmed_abstract(id_)
uid += 1
passage = {
"id": uid,
"type": "",
"text": [abstract_text],
"offsets": [[0, len(abstract_text)]],
}

for row in df.loc[df.doc_id == id_].itertuples():
uid += 1
if row.protein_or_nucleotide_sequence_mutation == "PSM":
ent_type = "Protein Sequence Mutation"
else:
ent_type = "Nucleotide Sequence Mutation"
tag_start, tag_end = int(row.off1), int(row.off2)
if tag_start > len(article_title):
tag_start -= 1
tag_end -= 1
elist.append(
{
"id": str(uid),
"type": ent_type,
"text": [row.covered_text],
"offsets": [[int(row.off1), int(row.off2)]],
"offsets": [[tag_start, tag_end]],
"normalized": [{"db_name": "dbSNP", "db_id": row.dbSNP_id}],
}
)
yield id_, {
"id": id_, # uid is an unique identifier for every record that starts from 1
"document_id": str(row[0]),
"entities": elist,
"passages": [],
"passages": [passage],
"events": [],
"coreferences": [],
"relations": [],
}


# This template is based on the following template from the datasets package:
# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py