Upload pervasive_imdb.py

pervasive_imdb.py

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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
+            })