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README.md

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----
-license: cc-by-sa-3.0
-task_categories:
-- image-classification
-language:
-- en
-pretty_name: mnist_ambigous
-size_categories:
-- 10K<n<100K
-source_datasets:
-- extended|mnist
-annotations_creators:
-- machine-generated
----
-
-
-# Mnist-Ambiguous
-
-This dataset contains mnist-like images, but with an unclear ground truth. For each image, there are two classes which could be considered true.
-Robust and uncertainty-aware DNNs should thus detect and flag these issues.
-
-### Features
-Same as mnist, the supervised dataset has an `image` (28x28 int array) and a `label` (int).
-
-Additionally, the following features are exposed for your convenience:
-
-- `text_label` (str): A textual representation of the probabilistic label, e.g. `p(Pullover)=0.54, p(Shirt)=0.46` 
-- `p_label` (list of floats): Ground-Truth probabilities for each class (two nonzero values for our ambiguous images)
-- `is_ambiguous` (bool): Flag indicating if this is one of our ambiguous images (see 'splits' below)
-
-### Splits
-We provide four splits:
-
-- `test`: 10'000 ambiguous images
-- `train`: 10'000 ambiguous images - adding ambiguous images to the training set makes sure test-time ambiguous images are in-distribution.
-- `test_mixed`: 20'000 images, consisting of the (shuffled) concatenation of our ambiguous `test` test and the nominal mnist test set by LeCun et. al.,
-- `train_mixed`: 70'000 images, consisting
-
-For research targeting explicitly aleatoric uncertainty, we recommend training the model using `train_mixed`. 
-Otherwise, our `test` set will lead to both epistemic and aleatoric uncertainty. 
-Note that in related literature, these 'mixed' splits are sometimes denoted as *dirty* splits.
-
-### Assessment and Validity
-For a brief discussion of the strength and weaknesses of this dataset, 
-including a quantitative comparison to the (only) other ambiguous datasets available in the literature, we refer to our paper.
-
-### Paper
-Pre-print here: [https://arxiv.org/abs/2207.10495](https://arxiv.org/abs/2207.10495)
-
-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}
-}
-```
-
-### License
-As this is a derivative work of mnist, which is CC-BY-SA 3.0 licensed, our dataset is released using the same license.
-
-

mninst_ambiguous/mnist_ambiguous-test.parquet

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mnist_ambiguous.py

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-"""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 = [
-    "T - shirt / top",
-    "Trouser",
-    "Pullover",
-    "Dress",
-    "Coat",
-    "Sandal",
-    "Shirt",
-    "Sneaker",
-    "Bag",
-    "Ankle boot",
-]
-
-
-class MnistAmbiguous(datasets.GeneratorBasedBuilder):
-    """An ambiguous mnist data set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="mninst_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
-