- Описание :
Наборы данных Istella — это три крупномасштабных набора данных Learning-to-Rank, выпущенных Istella. Каждый набор данных состоит из пар запрос-документ, представленных в виде векторов признаков и соответствующих меток суждения о релевантности.
Набор данных содержит три версии:
-
main
("Istella LETOR"): содержит 10 454 629 пар запрос-документ. -
s
("Istella-S LETOR"): содержит 3 408 630 пар запрос-документ. -
x
("Istella-X LETOR"): содержит 26 791 447 пар запрос-документ.
Вы можете указать, использовать ли main
, s
или x
версию набора данных следующим образом:
ds = tfds.load("istella/main")
ds = tfds.load("istella/s")
ds = tfds.load("istella/x")
Если указана только istella
, по умолчанию выбирается опция istella/main
:
# This is the same as `tfds.load("istella/main")`
ds = tfds.load("istella")
Домашняя страница : http://quickrank.isti.cnr.it/istella-dataset/
Исходный код :
tfds.ranking.istella.Istella
Версии :
-
1.0.0
: Первоначальный выпуск. -
1.0.1
(по умолчанию): исправить сериализацию для поддержки float64.
-
Автоматическое кэширование ( документация ): Нет
Особенности :
FeaturesDict({
'feature_1': Tensor(shape=(None,), dtype=tf.float64),
'feature_10': Tensor(shape=(None,), dtype=tf.float64),
'feature_100': Tensor(shape=(None,), dtype=tf.float64),
'feature_101': Tensor(shape=(None,), dtype=tf.float64),
'feature_102': Tensor(shape=(None,), dtype=tf.float64),
'feature_103': Tensor(shape=(None,), dtype=tf.float64),
'feature_104': Tensor(shape=(None,), dtype=tf.float64),
'feature_105': Tensor(shape=(None,), dtype=tf.float64),
'feature_106': Tensor(shape=(None,), dtype=tf.float64),
'feature_107': Tensor(shape=(None,), dtype=tf.float64),
'feature_108': Tensor(shape=(None,), dtype=tf.float64),
'feature_109': Tensor(shape=(None,), dtype=tf.float64),
'feature_11': Tensor(shape=(None,), dtype=tf.float64),
'feature_110': Tensor(shape=(None,), dtype=tf.float64),
'feature_111': Tensor(shape=(None,), dtype=tf.float64),
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'feature_117': Tensor(shape=(None,), dtype=tf.float64),
'feature_118': Tensor(shape=(None,), dtype=tf.float64),
'feature_119': Tensor(shape=(None,), dtype=tf.float64),
'feature_12': Tensor(shape=(None,), dtype=tf.float64),
'feature_120': Tensor(shape=(None,), dtype=tf.float64),
'feature_121': Tensor(shape=(None,), dtype=tf.float64),
'feature_122': Tensor(shape=(None,), dtype=tf.float64),
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'feature_197': Tensor(shape=(None,), dtype=tf.float64),
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'feature_199': Tensor(shape=(None,), dtype=tf.float64),
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'feature_210': Tensor(shape=(None,), dtype=tf.float64),
'feature_211': Tensor(shape=(None,), dtype=tf.float64),
'feature_212': Tensor(shape=(None,), dtype=tf.float64),
'feature_213': Tensor(shape=(None,), dtype=tf.float64),
'feature_214': Tensor(shape=(None,), dtype=tf.float64),
'feature_215': Tensor(shape=(None,), dtype=tf.float64),
'feature_216': Tensor(shape=(None,), dtype=tf.float64),
'feature_217': Tensor(shape=(None,), dtype=tf.float64),
'feature_218': Tensor(shape=(None,), dtype=tf.float64),
'feature_219': Tensor(shape=(None,), dtype=tf.float64),
'feature_22': Tensor(shape=(None,), dtype=tf.float64),
'feature_220': Tensor(shape=(None,), dtype=tf.float64),
'feature_23': Tensor(shape=(None,), dtype=tf.float64),
'feature_24': Tensor(shape=(None,), dtype=tf.float64),
'feature_25': Tensor(shape=(None,), dtype=tf.float64),
'feature_26': Tensor(shape=(None,), dtype=tf.float64),
'feature_27': Tensor(shape=(None,), dtype=tf.float64),
'feature_28': Tensor(shape=(None,), dtype=tf.float64),
'feature_29': Tensor(shape=(None,), dtype=tf.float64),
'feature_3': Tensor(shape=(None,), dtype=tf.float64),
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'feature_31': Tensor(shape=(None,), dtype=tf.float64),
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'feature_36': Tensor(shape=(None,), dtype=tf.float64),
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'feature_38': Tensor(shape=(None,), dtype=tf.float64),
'feature_39': Tensor(shape=(None,), dtype=tf.float64),
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'feature_41': Tensor(shape=(None,), dtype=tf.float64),
'feature_42': Tensor(shape=(None,), dtype=tf.float64),
'feature_43': Tensor(shape=(None,), dtype=tf.float64),
'feature_44': Tensor(shape=(None,), dtype=tf.float64),
'feature_45': Tensor(shape=(None,), dtype=tf.float64),
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'feature_50': Tensor(shape=(None,), dtype=tf.float64),
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'feature_60': Tensor(shape=(None,), dtype=tf.float64),
'feature_61': Tensor(shape=(None,), dtype=tf.float64),
'feature_62': Tensor(shape=(None,), dtype=tf.float64),
'feature_63': Tensor(shape=(None,), dtype=tf.float64),
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'feature_65': Tensor(shape=(None,), dtype=tf.float64),
'feature_66': Tensor(shape=(None,), dtype=tf.float64),
'feature_67': Tensor(shape=(None,), dtype=tf.float64),
'feature_68': Tensor(shape=(None,), dtype=tf.float64),
'feature_69': Tensor(shape=(None,), dtype=tf.float64),
'feature_7': Tensor(shape=(None,), dtype=tf.float64),
'feature_70': Tensor(shape=(None,), dtype=tf.float64),
'feature_71': Tensor(shape=(None,), dtype=tf.float64),
'feature_72': Tensor(shape=(None,), dtype=tf.float64),
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'feature_75': Tensor(shape=(None,), dtype=tf.float64),
'feature_76': Tensor(shape=(None,), dtype=tf.float64),
'feature_77': Tensor(shape=(None,), dtype=tf.float64),
'feature_78': Tensor(shape=(None,), dtype=tf.float64),
'feature_79': Tensor(shape=(None,), dtype=tf.float64),
'feature_8': Tensor(shape=(None,), dtype=tf.float64),
'feature_80': Tensor(shape=(None,), dtype=tf.float64),
'feature_81': Tensor(shape=(None,), dtype=tf.float64),
'feature_82': Tensor(shape=(None,), dtype=tf.float64),
'feature_83': Tensor(shape=(None,), dtype=tf.float64),
'feature_84': Tensor(shape=(None,), dtype=tf.float64),
'feature_85': Tensor(shape=(None,), dtype=tf.float64),
'feature_86': Tensor(shape=(None,), dtype=tf.float64),
'feature_87': Tensor(shape=(None,), dtype=tf.float64),
'feature_88': Tensor(shape=(None,), dtype=tf.float64),
'feature_89': Tensor(shape=(None,), dtype=tf.float64),
'feature_9': Tensor(shape=(None,), dtype=tf.float64),
'feature_90': Tensor(shape=(None,), dtype=tf.float64),
'feature_91': Tensor(shape=(None,), dtype=tf.float64),
'feature_92': Tensor(shape=(None,), dtype=tf.float64),
'feature_93': Tensor(shape=(None,), dtype=tf.float64),
'feature_94': Tensor(shape=(None,), dtype=tf.float64),
'feature_95': Tensor(shape=(None,), dtype=tf.float64),
'feature_96': Tensor(shape=(None,), dtype=tf.float64),
'feature_97': Tensor(shape=(None,), dtype=tf.float64),
'feature_98': Tensor(shape=(None,), dtype=tf.float64),
'feature_99': Tensor(shape=(None,), dtype=tf.float64),
'label': Tensor(shape=(None,), dtype=tf.float64),
})
Ключи под наблюдением (см . документ
as_supervised
):None
Рисунок ( tfds.show_examples ): не поддерживается.
Цитата :
@article{10.1145/2987380,
author = {Dato, Domenico and Lucchese, Claudio and Nardini, Franco Maria and Orlando, Salvatore and Perego, Raffaele and Tonellotto, Nicola and Venturini, Rossano},
title = {Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees},
year = {2016},
publisher = {ACM},
address = {New York, NY, USA},
volume = {35},
number = {2},
issn = {1046-8188},
url = {https://doi.org/10.1145/2987380},
doi = {10.1145/2987380},
journal = {ACM Transactions on Information Systems},
articleno = {15},
numpages = {31},
}
istella/main (конфигурация по умолчанию)
Размер загрузки :
1.20 GiB
Размер набора данных :
1.40 GiB
Сплиты :
Расколоть | Примеры |
---|---|
'test' | 9799 |
'train' | 23 219 |
- Примеры ( tfds.as_dataframe ):
истелла / с
Размер загрузки :
450.26 MiB
Размер набора данных :
728.40 MiB
.Сплиты :
Расколоть | Примеры |
---|---|
'test' | 6562 |
'train' | 19 245 |
'vali' | 7 211 |
- Примеры ( tfds.as_dataframe ):
истелла/х
Размер загрузки :
4.42 GiB
Размер набора данных :
2.06 GiB
Сплиты :
Расколоть | Примеры |
---|---|
'test' | 2000 |
'train' | 6000 |
'vali' | 2000 |
- Примеры ( tfds.as_dataframe ):