Datasets:

mweiss
/

mnist_ambiguous

	"""An ambiguous mnist data set"""

	import csv

	import datasets
	import numpy as np
	from datasets.tasks import ImageClassification

	_CITATION = """\
	@misc{https://doi.org/10.48550/arxiv.2207.10495,
	doi = {10.48550/ARXIV.2207.10495},
	url = {https://arxiv.org/abs/2207.10495},
	author = {Weiss, Michael and Gómez, André García and Tonella, Paolo},
	title = {A Forgotten Danger in DNN Supervision Testing: Generating and Detecting True Ambiguity},
	publisher = {arXiv},
	year = {2022}
	}
	"""

	_DESCRIPTION = """\
	The images were created such that they have an unclear ground truth,
	i.e., such that they are similar to multiple - but not all - of the datasets classes.
	Robust and uncertainty-aware models should be able to detect and flag these ambiguous images.
	As such, the dataset should be merged / mixed with the original dataset and we
	provide such 'mixed' splits for convenience. Please refer to the dataset card for details.
	"""

	_HOMEPAGE = "https://github.com/testingautomated-usi/ambiguous-datasets"
	_LICENSE = "https://raw.githubusercontent.com/testingautomated-usi/ambiguous-datasets/main/LICENSE"

	_VERSION = "0.1.0"
	_URL = f"https://github.com/testingautomated-usi/ambiguous-datasets/releases/download/v{_VERSION}/"

	_URLS = {
	"train": "mnist-test.csv",
	"test": "mnist-test.csv",
	}

	_NAMES = list(range(10))


	class MnistAmbiguous(datasets.GeneratorBasedBuilder):
	"""An ambiguous mnist data set"""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="mnist_ambiguous",
	version=datasets.Version(_VERSION),
	description=_DESCRIPTION,
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"label": datasets.features.ClassLabel(names=_NAMES),
	"text_label": datasets.Value("string"),
	"p_label": datasets.Sequence(datasets.Value("float32"), length=10),
	"is_ambiguous": datasets.Value("bool"),
	}
	),
	supervised_keys=("image", "label"),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	task_templates=[ImageClassification(image_column="image", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
	downloaded_files = dl_manager.download(urls_to_download)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": downloaded_files["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": downloaded_files["test"],
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name="train_mixed",
	gen_kwargs={
	"filepath": downloaded_files["train"],
	"split": "train_mixed",
	},
	),
	datasets.SplitGenerator(
	name="test_mixed",
	gen_kwargs={
	"filepath": downloaded_files["test"],
	"split": "test_mixed",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	"""This function returns the examples in the raw form."""

	def _gen_amb_images():
	with open(filepath) as csvfile:
	spamreader = csv.reader(csvfile, delimiter=',', quotechar='"')
	for i, row in enumerate(spamreader):
	if i == 0:
	continue

	det_label = int(row[7])
	class_1, class_2 = int(row[3]), int(row[4])
	p_1, p_2 = float(row[5]), float(row[6])
	text_label = f"p({_NAMES[class_1]})={p_1:.2f}, p({_NAMES[class_2]})={p_2:.2f}"

	p_label = [0.0] * 10
	p_label[class_1] = p_1
	p_label[class_2] = p_2

	image = np.array(row[9:], dtype=np.uint8).reshape(28, 28)

	yield i, {"image": image, "label": det_label,
	"text_label": text_label, "p_label": p_label, "is_ambiguous": True}

	if split == "test" or split == "train":
	yield from _gen_amb_images()

	elif split == "test_mixed" or split == "train_mixed":

	nominal_samples = []
	nom_split = "test" if split == "test_mixed" else "train"
	nominal_dataset = datasets.load_dataset("mnist", split=nom_split)
	for x in nominal_dataset:
	nominal_samples.append({
	"image": np.array(x["image"]),
	"label": x["label"],
	"text_label": f"p({_NAMES[x['label']]})=1",
	"p_label": [1.0 if i == x["label"] else 0.0 for i in range(10)],
	"is_ambiguous": False
	})

	ambiguous_samples = list([x for i, x in _gen_amb_images()])
	all_samples = nominal_samples + ambiguous_samples
	np.random.RandomState(42).shuffle(all_samples)

	for i, x in enumerate(all_samples):
	yield i, x