File size: 15,610 Bytes
ba48b51 9fd81e4 10951b9 ba48b51 e0765cf a198b93 10951b9 ba48b51 a198b93 ba48b51 a198b93 ba48b51 a198b93 ba48b51 a198b93 ba48b51 a198b93 ba48b51 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 |
import datasets
import json
logger = datasets.logging.get_logger(__name__)
USERNAME = "Dakhoo"
REPO_NAME = "L2T-NeurIPS-2023"
LOCAL = False
_CITATION = """\
@article{bender2023learning,
title={Learning to Taste: A Multimodal Wine Dataset},
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},
journal={arXiv preprint arXiv:2308.16900},
year={2023}
}
"""
_DESCRIPTION = (
"The dataset encompasses 897k images of wine labels and 824k reviews of wines "
"curated from the Vivino platform. It has over 350k unique vintages, annotated "
"with year, region, rating, alcohol percentage, price, and grape composition. "
"We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment "
"with 256 participants who were asked to rank wines based on their similarity in flavor, "
"resulting in more than 5k pairwise flavor distances."
)
_HOMEPAGE = "https://https://thoranna.github.io/learning_to_taste/"
_LICENSE = """\
LICENSE AGREEMENT
=================
- WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,
Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence
"""
reviews = ['Deliciously fragrant xxx',
'Barolo & Brunello Tasting with Janne',
'Oak',
'Muito bom. Foi uma agradável surpresa. Óptimo sabor e guloso a acompanhar o almoço. Recomendo. ',
'Flauw zwoele smaak zonder al teveel afdronk. Voor de prijs oké zonder meer. Ik ben geen fan. ',
'Very different, very pink. Quite fruity can feel at the back sides of tongue ',
'Honey, apricot, tinned peaches in syrup. Oily, silky texture. Sweetness is well balanced with acidity. ',
'Amazing fruit and great finish. ',
'Dry, floral nose with fruit on the back',
'This Riesling Kabinett was good. Had a few minor problems, but cant complain to much at $13 ',
'Such an unusual drop, honey, spice notes. Drank it chilled. Nose like the skin on a sauccison...!',
'Very sweet and light bubbly red wine ',
'Great value. Really enjoyable wine and went down a treat with a steak 👌🏻',
'',
'Quite refreshing with a light citrus taste.',
'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',
'Light, dry, grapefruit flavor, delicious ']
_REPO = f"https://huggingface.co/datasets/{USERNAME}/{REPO_NAME}/resolve/main/"
_REPO_META = f"https://huggingface.co/datasets/{USERNAME}/{REPO_NAME}/resolve/main/metadata"
if LOCAL:
_REPO = f"/Users/alka/Devel/L2T-NeurIPS-2023"
class WineSensedConfig(datasets.BuilderConfig):
"""BuilderConfig for WineSensed."""
def __init__(self, data_url, metadata_urls, **kwargs):
"""BuilderConfig for WineSensed.
Args:
data_url: `string`, url to download the zip file from.
matadata_urls: dictionary with 'train' containing the metadata URLs
**kwargs: keyword arguments forwarded to super.
"""
super(WineSensedConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_url = data_url
self.metadata_urls = metadata_urls
class WineSensed(datasets.GeneratorBasedBuilder):
"""WineSensed Images dataset"""
BUILDER_CONFIGS = [
WineSensedConfig(
name="vintages",
description="All tasted vintages along with their attributions.",
data_url=f"{_REPO}/data/vintages/vintages_dataset.tar.gz",
metadata_urls={
"train": f"{_REPO_META}/vintages/train.txt",
},
),
WineSensedConfig(
name="napping_participants",
description="Napping and Participants datasets",
data_url=f"{_REPO}/data/napping_participants/napping_participants.tar.gz",
metadata_urls={
"train": f"{_REPO_META}/napping_participants/train.txt",
},
),
WineSensedConfig(
name="small",
description="Small dataset.",
data_url=f"{_REPO}/data/small/small.tar.gz",
metadata_urls={
"train": f"{_REPO_META}/small/small_dataset.jsonl",
},
),
WineSensedConfig(
name="wt_session",
description="Image-Review dataset.",
data_url=f"{_REPO}/data/wt_session/wt_session.tar.gz",
metadata_urls={
"train": f"{_REPO_META}/wt_session/wt_session.jsonl",
},
),
WineSensedConfig(
name="all",
description="All images.",
data_url=f"{_REPO}/data/all/all.tar.gz",
metadata_urls={
"train": f"{_REPO_META}/all/all_dataset.jsonl",
},
),
]
def _info(self):
if self.config.name == 'vintages':
features = datasets.Features(
{
"vintage_id": datasets.Value("string"),
"year": datasets.Value("string"),
"winery_id": datasets.Value("string"),
"wine_alcohol": datasets.Value("string"),
"country": datasets.Value("string"),
"region": datasets.Value("string"),
"price": datasets.Value("string"),
"rating": datasets.Value("string"),
"grape": datasets.Value("string"),
}
)
elif self.config.name == 'napping_participants':
features = datasets.Features(
{
"event_name": datasets.Value("string"),
"session_round_name": datasets.Value("string"),
"experiment_no": datasets.Value("string"),
"round_id": datasets.Value("string"),
"participant_id": datasets.Value("string"),
"experiment_id": datasets.Value("string"),
"coor1": datasets.Value("string"),
"coor2": datasets.Value("string"),
"color": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"image": datasets.Image(),
"vintage_id": datasets.Value("string"),
"year": datasets.Value("string"),
"winery_id": datasets.Value("string"),
"wine_alcohol": datasets.Value("string"),
"country": datasets.Value("string"),
"region": datasets.Value("string"),
"price": datasets.Value("string"),
"rating": datasets.Value("string"),
"grape": datasets.Value("string"),
"review": datasets.Value("string"),
"event_name": datasets.Value("string"),
"session_round_name": datasets.Value("string"),
"experiment_no": datasets.Value("string"),
"round_id": datasets.Value("string"),
"participant_id": datasets.Value("string"),
"experiment_id": datasets.Value("string"),
"coor1": datasets.Value("string"),
"coor2": datasets.Value("string"),
"color": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION + self.config.description,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download(self.config.data_url)
metadata_paths = dl_manager.download(self.config.metadata_urls)
record_iters = dl_manager.iter_archive(archive_path)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"records": record_iters,
"metadata_path": metadata_paths["train"],
},
),
]
def _generate_examples(self, records, metadata_path):
"""Generate images and metadata for splits."""
# Process the JSONL file to extract all metadata
if self.config.name == 'vintages':
for idx, (filepath, image) in enumerate(records):
file_jsonl = image.read()
jsonl_string = file_jsonl.decode('utf-8')
json_objects = jsonl_string.strip().split('\n')
id = 0
for json_object in json_objects:
data_dict = json.loads(json_object)
yield id, {
"vintage_id": data_dict['vintage_id'],
"year": data_dict['year'],
"winery_id": data_dict['winery_id'],
"wine_alcohol": data_dict['wine_alcohol'],
"country": data_dict['country'],
"region": data_dict['region'],
"price": data_dict['price'],
"rating": data_dict['rating'],
"grape": data_dict['grape'],
}
id += 1
elif self.config.name == 'napping_participants':
for idx, (filepath, image) in enumerate(records):
file_jsonl = image.read()
jsonl_string = file_jsonl.decode('utf-8')
json_objects = jsonl_string.strip().split('\n')
id = 0
for json_object in json_objects:
data_dict = json.loads(json_object)
yield id, {
"event_name": data_dict['event_name'],
"session_round_name": data_dict['session_round_name'],
"experiment_no": data_dict['experiment_no'],
"round_id": data_dict['round_id'],
"participant_id": data_dict['participant_id'],
"experiment_id": data_dict['experiment_id'],
"coor1": data_dict['coor1'],
"coor2": data_dict['coor2'],
"color": data_dict['color'],
}
id += 1
else:
metadata_dict = self._process_images_jsonl_file(metadata_path)
for idx, (filepath, image) in enumerate(records):
yield idx, {
"image": {"path": filepath, "bytes": image.read()},
"vintage_id": metadata_dict.get(filepath.split('/')[1], {}).get('vintage_id', None),
"year": metadata_dict.get(filepath.split('/')[1], {}).get('year', None),
"winery_id": metadata_dict.get(filepath.split('/')[1], {}).get('winery_id', None),
"wine_alcohol": metadata_dict.get(filepath.split('/')[1], {}).get('wine_alcohol', None),
"country": metadata_dict.get(filepath.split('/')[1], {}).get('country', None),
"region": metadata_dict.get(filepath.split('/')[1], {}).get('region', None),
"price": metadata_dict.get(filepath.split('/')[1], {}).get('price', None),
"rating": metadata_dict.get(filepath.split('/')[1], {}).get('rating', None),
"grape": metadata_dict.get(filepath.split('/')[1], {}).get('grape', None),
"review": metadata_dict.get(filepath.split('/')[1], {}).get('review', None),
"event_name": metadata_dict.get(filepath.split('/')[1], {}).get('event_name', None),
"session_round_name": metadata_dict.get(filepath.split('/')[1], {}).get('session_round_name', None),
"experiment_no": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_no', None),
"round_id": metadata_dict.get(filepath.split('/')[1], {}).get('round_id', None),
"participant_id": metadata_dict.get(filepath.split('/')[1], {}).get('participant_id', None),
"experiment_id": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_id', None),
"coor1": metadata_dict.get(filepath.split('/')[1], {}).get('coor1', None),
"coor2": metadata_dict.get(filepath.split('/')[1], {}).get('coor2', None),
"color": metadata_dict.get(filepath.split('/')[1], {}).get('color', None),
}
def _process_images_jsonl_file(self, jsonl_file_path):
"""A utility function defined within the WineSensed class.
This function reads and processes a JSONL (JSON Lines) file containing metadata about images and reviews.
It iterates through the lines in the JSONL file, parsing each line as JSON data.
For each JSON object in the file, it extracts relevant information such as image paths, reviews, vintage IDs, and more.
The extracted information is stored in a dictionary called metadata_dict, which is returned by the function. """
metadata_dict = {}
with open(jsonl_file_path, 'r', encoding="utf-8") as jsonl_file:
for line in jsonl_file:
try:
data = json.loads(line)
image = data.get('image', None)
# Check if 'image' is present in the JSON object
if image is not None:
metadata_dict[image] = {
"review": data.get('review', None),
"vintage_id": data.get('vintage_id', None),
"experiment_id": data.get('experiment_id', None),
"year": data.get('year', None),
"winery_id": data.get('winery_id', None),
"wine_alcohol": data.get('wine_alcohol', None),
"country": data.get('country', None),
"region": data.get('region', None),
"price": data.get('price', None),
"rating": data.get('rating', None),
"grape": data.get('grape', None),
"event_name": data.get('event_name', None),
"session_round_name": data.get('session_round_name', None),
"experiment_no": data.get('experiment_no', None),
"round_id": data.get('round_id', None),
"participant_id": data.get('participant_id', None),
"experiment_id": data.get('experiment_id', None),
"coor1": data.get('coor1', None),
"coor2": data.get('coor2', None),
"color": data.get('color', None),
}
except json.JSONDecodeError as e:
print(f"Error parsing JSON: {e}")
return metadata_dict |