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import datasets |
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import json |
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logger = datasets.logging.get_logger(__name__) |
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USERNAME = "Dakhoo" |
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REPO_NAME = "L2T-NeurIPS-2023" |
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LOCAL = False |
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_CITATION = """\ |
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@article{bender2023learning, |
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title={Learning to Taste: A Multimodal Wine Dataset}, |
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author={Bender, Thoranna and S{\o}rensen, Simon M{\o}e and Kashani, Alireza and Hjorleifsson, K Eldjarn and Hyldig, Grethe and Hauberg, S{\o}ren and Belongie, Serge and Warburg, Frederik}, |
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journal={arXiv preprint arXiv:2308.16900}, |
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year={2023} |
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} |
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""" |
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_DESCRIPTION = ( |
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"The dataset encompasses 897k images of wine labels and 824k reviews of wines " |
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"curated from the Vivino platform. It has over 350k unique vintages, annotated " |
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"with year, region, rating, alcohol percentage, price, and grape composition. " |
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"We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment " |
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"with 256 participants who were asked to rank wines based on their similarity in flavor, " |
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"resulting in more than 5k pairwise flavor distances." |
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) |
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_HOMEPAGE = "https://https://thoranna.github.io/learning_to_taste/" |
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_LICENSE = """\ |
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LICENSE AGREEMENT |
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================= |
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- WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig, |
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Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence |
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""" |
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reviews = ['Deliciously fragrant xxx', |
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'Barolo & Brunello Tasting with Janne', |
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'Oak', |
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'Muito bom. Foi uma agradável surpresa. Óptimo sabor e guloso a acompanhar o almoço. Recomendo. ', |
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'Flauw zwoele smaak zonder al teveel afdronk. Voor de prijs oké zonder meer. Ik ben geen fan. ', |
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'Very different, very pink. Quite fruity can feel at the back sides of tongue ', |
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'Honey, apricot, tinned peaches in syrup. Oily, silky texture. Sweetness is well balanced with acidity. ', |
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'Amazing fruit and great finish. ', |
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'Dry, floral nose with fruit on the back', |
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'This Riesling Kabinett was good. Had a few minor problems, but cant complain to much at $13 ', |
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'Such an unusual drop, honey, spice notes. Drank it chilled. Nose like the skin on a sauccison...!', |
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'Very sweet and light bubbly red wine ', |
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'Great value. Really enjoyable wine and went down a treat with a steak 👌🏻', |
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'', |
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'Quite refreshing with a light citrus taste.', |
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'Pours in dark amber colour with excellant lacing. Aroma of raisins, caramel. Highly sweet, medium sour, light bitterness, taste of nutts, raisins. Full bodied, thick feel, long lasting aftertaste', |
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'Light, dry, grapefruit flavor, delicious '] |
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_REPO = f"https://huggingface.co/datasets/{USERNAME}/{REPO_NAME}/resolve/main/" |
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_REPO = "https://huggingface.co/datasets/Dakhoo/L2T-NeurIPS-2023/resolve/main/" |
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if LOCAL: |
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_REPO = f"/Users/alka/Devel/L2T-NeurIPS-2023" |
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class WineSensedConfig(datasets.BuilderConfig): |
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"""BuilderConfig for WineSensed.""" |
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def __init__(self, data_url, metadata_urls, **kwargs): |
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"""BuilderConfig for WineSensed. |
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Args: |
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data_url: `string`, url to download the zip file from. |
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matadata_urls: dictionary with 'train' containing the metadata URLs |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(WineSensedConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs) |
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self.data_url = data_url |
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self.metadata_urls = metadata_urls |
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class WineSensed(datasets.GeneratorBasedBuilder): |
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"""WineSensed Images dataset""" |
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BUILDER_CONFIGS = [ |
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WineSensedConfig( |
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name="vintages", |
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description="All tasted vintages along with their attributions.", |
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data_url=f"{_REPO}/data/vintages/vintages_dataset.tar.gz", |
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metadata_urls={ |
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"train": f"{_REPO}/data/vintages/train.txt", |
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}, |
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), |
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WineSensedConfig( |
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name="napping_participants", |
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description="Napping and Participants datasets", |
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data_url=f"{_REPO}/data/napping_participants/napping_participants.tar.gz", |
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metadata_urls={ |
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"train": f"{_REPO}/data/napping_participants/train.txt", |
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}, |
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), |
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WineSensedConfig( |
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name="small", |
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description="Small dataset.", |
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data_url=f"{_REPO}/data/small/small.tar.gz", |
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metadata_urls={ |
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"train": f"{_REPO}/data/small/small_dataset.jsonl", |
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}, |
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), |
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WineSensedConfig( |
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name="wt_session", |
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description="Image-Review dataset.", |
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data_url=f"{_REPO}/data/wt_session/wt_session.tar.gz", |
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metadata_urls={ |
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"train": f"{_REPO}/data/wt_session/wt_session.jsonl", |
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}, |
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), |
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WineSensedConfig( |
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name="all", |
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description="All images.", |
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data_url=f"{_REPO}/data/all/all.tar.gz", |
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metadata_urls={ |
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"train": f"{_REPO}/data/all/all_dataset.jsonl", |
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}, |
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), |
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] |
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def _info(self): |
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if self.config.name == 'vintages': |
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features = datasets.Features( |
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{ |
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"vintage_id": datasets.Value("string"), |
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"year": datasets.Value("string"), |
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"winery_id": datasets.Value("string"), |
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"wine_alcohol": datasets.Value("string"), |
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"country": datasets.Value("string"), |
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"region": datasets.Value("string"), |
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"price": datasets.Value("string"), |
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"rating": datasets.Value("string"), |
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"grape": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == 'napping_participants': |
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features = datasets.Features( |
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{ |
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"event_name": datasets.Value("string"), |
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"session_round_name": datasets.Value("string"), |
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"experiment_no": datasets.Value("string"), |
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"round_id": datasets.Value("string"), |
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"participant_id": datasets.Value("string"), |
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"experiment_id": datasets.Value("string"), |
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"coor1": datasets.Value("string"), |
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"coor2": datasets.Value("string"), |
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"color": datasets.Value("string"), |
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} |
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) |
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else: |
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features = datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"vintage_id": datasets.Value("string"), |
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"year": datasets.Value("string"), |
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"winery_id": datasets.Value("string"), |
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"wine_alcohol": datasets.Value("string"), |
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"country": datasets.Value("string"), |
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"region": datasets.Value("string"), |
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"price": datasets.Value("string"), |
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"rating": datasets.Value("string"), |
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"grape": datasets.Value("string"), |
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"review": datasets.Value("string"), |
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"event_name": datasets.Value("string"), |
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"session_round_name": datasets.Value("string"), |
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"experiment_no": datasets.Value("string"), |
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"round_id": datasets.Value("string"), |
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"participant_id": datasets.Value("string"), |
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"experiment_id": datasets.Value("string"), |
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"coor1": datasets.Value("string"), |
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"coor2": datasets.Value("string"), |
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"color": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION + self.config.description, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(self.config.data_url) |
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metadata_paths = dl_manager.download(self.config.metadata_urls) |
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record_iters = dl_manager.iter_archive(archive_path) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"records": record_iters, |
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"metadata_path": metadata_paths["train"], |
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}, |
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), |
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] |
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def _generate_examples(self, records, metadata_path): |
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"""Generate images and metadata for splits.""" |
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if self.config.name == 'vintages': |
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for idx, (filepath, image) in enumerate(records): |
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file_jsonl = image.read() |
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jsonl_string = file_jsonl.decode('utf-8') |
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json_objects = jsonl_string.strip().split('\n') |
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id = 0 |
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for json_object in json_objects: |
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data_dict = json.loads(json_object) |
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yield id, { |
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"vintage_id": data_dict['vintage_id'], |
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"year": data_dict['year'], |
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"winery_id": data_dict['winery_id'], |
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"wine_alcohol": data_dict['wine_alcohol'], |
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"country": data_dict['country'], |
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"region": data_dict['region'], |
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"price": data_dict['price'], |
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"rating": data_dict['rating'], |
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"grape": data_dict['grape'], |
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} |
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id += 1 |
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elif self.config.name == 'napping_participants': |
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for idx, (filepath, image) in enumerate(records): |
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file_jsonl = image.read() |
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jsonl_string = file_jsonl.decode('utf-8') |
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json_objects = jsonl_string.strip().split('\n') |
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id = 0 |
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for json_object in json_objects: |
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data_dict = json.loads(json_object) |
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yield id, { |
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"event_name": data_dict['event_name'], |
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"session_round_name": data_dict['session_round_name'], |
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"experiment_no": data_dict['experiment_no'], |
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"round_id": data_dict['round_id'], |
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"participant_id": data_dict['participant_id'], |
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"experiment_id": data_dict['experiment_id'], |
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"coor1": data_dict['coor1'], |
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"coor2": data_dict['coor2'], |
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"color": data_dict['color'], |
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} |
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id += 1 |
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else: |
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metadata_dict = self._process_images_jsonl_file(metadata_path) |
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for idx, (filepath, image) in enumerate(records): |
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yield idx, { |
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"image": {"path": filepath, "bytes": image.read()}, |
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"vintage_id": metadata_dict.get(filepath.split('/')[1], {}).get('vintage_id', None), |
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"year": metadata_dict.get(filepath.split('/')[1], {}).get('year', None), |
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"winery_id": metadata_dict.get(filepath.split('/')[1], {}).get('winery_id', None), |
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"wine_alcohol": metadata_dict.get(filepath.split('/')[1], {}).get('wine_alcohol', None), |
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"country": metadata_dict.get(filepath.split('/')[1], {}).get('country', None), |
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"region": metadata_dict.get(filepath.split('/')[1], {}).get('region', None), |
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"price": metadata_dict.get(filepath.split('/')[1], {}).get('price', None), |
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"rating": metadata_dict.get(filepath.split('/')[1], {}).get('rating', None), |
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"grape": metadata_dict.get(filepath.split('/')[1], {}).get('grape', None), |
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"review": metadata_dict.get(filepath.split('/')[1], {}).get('review', None), |
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"event_name": metadata_dict.get(filepath.split('/')[1], {}).get('event_name', None), |
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"session_round_name": metadata_dict.get(filepath.split('/')[1], {}).get('session_round_name', None), |
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"experiment_no": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_no', None), |
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"round_id": metadata_dict.get(filepath.split('/')[1], {}).get('round_id', None), |
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"participant_id": metadata_dict.get(filepath.split('/')[1], {}).get('participant_id', None), |
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"experiment_id": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_id', None), |
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"coor1": metadata_dict.get(filepath.split('/')[1], {}).get('coor1', None), |
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"coor2": metadata_dict.get(filepath.split('/')[1], {}).get('coor2', None), |
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"color": metadata_dict.get(filepath.split('/')[1], {}).get('color', None), |
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} |
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def _process_images_jsonl_file(self, jsonl_file_path): |
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"""A utility function defined within the WineSensed class. |
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This function reads and processes a JSONL (JSON Lines) file containing metadata about images and reviews. |
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It iterates through the lines in the JSONL file, parsing each line as JSON data. |
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For each JSON object in the file, it extracts relevant information such as image paths, reviews, vintage IDs, and more. |
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The extracted information is stored in a dictionary called metadata_dict, which is returned by the function. """ |
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metadata_dict = {} |
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with open(jsonl_file_path, 'r', encoding="utf-8") as jsonl_file: |
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for line in jsonl_file: |
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try: |
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data = json.loads(line) |
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image = data.get('image', None) |
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if image is not None: |
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metadata_dict[image] = { |
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"review": data.get('review', None), |
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"vintage_id": data.get('vintage_id', None), |
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"experiment_id": data.get('experiment_id', None), |
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"year": data.get('year', None), |
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"winery_id": data.get('winery_id', None), |
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"wine_alcohol": data.get('wine_alcohol', None), |
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"country": data.get('country', None), |
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"region": data.get('region', None), |
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"price": data.get('price', None), |
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"rating": data.get('rating', None), |
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"grape": data.get('grape', None), |
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"event_name": data.get('event_name', None), |
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"session_round_name": data.get('session_round_name', None), |
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"experiment_no": data.get('experiment_no', None), |
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"round_id": data.get('round_id', None), |
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"participant_id": data.get('participant_id', None), |
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"experiment_id": data.get('experiment_id', None), |
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"coor1": data.get('coor1', None), |
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"coor2": data.get('coor2', None), |
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"color": data.get('color', None), |
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} |
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except json.JSONDecodeError as e: |
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print(f"Error parsing JSON: {e}") |
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return metadata_dict |
