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# 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.
from typing import List, Tuple, Dict
from pathlib import Path
import json
import os
import numpy as np
import datasets
_CITATION = """\
@inproceedings{DBLP:conf/nips/NorthcuttAM21,
author = {Curtis G. Northcutt and
Anish Athalye and
Jonas Mueller},
editor = {Joaquin Vanschoren and
Sai{-}Kit Yeung},
title = {Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks},
booktitle = {Proceedings of the Neural Information Processing Systems Track on
Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December
2021, virtual},
year = {2021},
url = {https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/f2217062e9a397a1dca429e7d70bc6ca-Abstract-round1.html},
timestamp = {Thu, 05 May 2022 16:53:59 +0200},
biburl = {https://dblp.org/rec/conf/nips/NorthcuttAM21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DATASETNAME = "pervasive_imdb"
_DESCRIPTION = """\
This dataset is designed for Annotation Error Detection.
"""
_HOMEPAGE = ""
_LICENSE = "GPL3"
_URLS = {
"imdb": "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
"mturk": "https://raw.githubusercontent.com/cleanlab/label-errors/main/mturk/imdb_mturk.json",
"indexing": "https://raw.githubusercontent.com/cleanlab/label-errors/main/dataset_indexing/imdb_test_set_index_to_filename.json"
}
_SOURCE_VERSION = "1.0.0"
_SCHEMA = datasets.Features({
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"label": datasets.Value("string"),
"true_label": datasets.Value("string"),
})
class InconsistenciesFlights(datasets.GeneratorBasedBuilder):
_VERSION = datasets.Version(_SOURCE_VERSION)
def _info(self) -> datasets.DatasetInfo:
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_SCHEMA,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
imdb_dir = dl_manager.download_and_extract(_URLS["imdb"])
mturk_file = dl_manager.download_and_extract(_URLS["mturk"])
indexing_file = dl_manager.download_and_extract(_URLS["indexing"])
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# Whatever you put in gen_kwargs will be passed to _generate_examples
gen_kwargs={
"imdb_dir": Path(imdb_dir) / "aclImdb",
"mturk_file": Path(mturk_file),
"indexing_file": Path(indexing_file)
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, imdb_dir: Path, mturk_file: Path, indexing_file: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
walk_order = {}
# We don't deal with train set indices, so any order is fine for the train set.
walk_order['train'] = [d + z for d in ["neg/", "pos/"] \
for z in os.listdir(imdb_dir / 'train' / d)]
# Test set walk order needs to match our order to map errors correctly.
with open(indexing_file, 'r') as rf:
walk_order['test'] = json.load(rf)
# This text dict stores the text data with keys ['train', 'test']
text = {}
# Read in text data for IMDB
for dataset in ['train', 'test']:
text[dataset] = []
dataset_dir = imdb_dir / dataset
for i, fn in enumerate(walk_order[dataset]):
with open(dataset_dir / fn, 'r') as rf:
text[dataset].append(rf.read())
idx_to_mturk = {}
with open(mturk_file) as f:
mturk_data = json.load(f)
for datapoint in mturk_data:
idx = walk_order['test'].index(datapoint['id'].removeprefix('test/') + ".txt")
idx_to_mturk[idx] = datapoint["mturk"]
# The given labels for both train and test set are the same.
labels = np.concatenate([np.zeros(12500), np.ones(12500)]).astype(int)
for i in range(25000):
if i in idx_to_mturk and idx_to_mturk[i]["given"] < 3:
true_label = not bool(labels[i])
else:
true_label = bool(labels[i])
yield (i, {
"id": str(i),
"text": text["test"][i],
"label": bool(labels[i]),
"true_label": true_label
})
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