Source code for cornac.datasets.tradesy
# Copyright 2018 The Cornac Authors. All Rights Reserved.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
Link to the data: http://jmcauley.ucsd.edu/data/tradesy/
This data is used in the VBPR paper. After cleaning the data, we have:
- Number of feedback: 394,421 (410,186 is reported but there are duplicates)
- Number of users: 19,243 (19,823 is reported due to duplicates)
- Number of items: 165,906 (166,521 is reported due to duplicates)
"""
from typing import List
import numpy as np
from ..utils import cache
from ..data import Reader
from ..data.reader import read_text
[docs]
def load_feedback(reader: Reader = None) -> List:
"""Load user-item feedback
Parameters
----------
reader: `obj:cornac.data.Reader`, default: None
Reader object used to read the data.
Returns
-------
data: array-like
Data in the form of a list of tuples (user, item, 1).
"""
fpath = cache(url='https://static.preferred.ai/cornac/datasets/tradesy/users.zip',
unzip=True, relative_path='tradesy/users.csv')
reader = Reader() if reader is None else reader
return reader.read(fpath, fmt='UI', sep=',')
[docs]
def load_visual_feature():
"""Load item visual features
Returns
-------
features: numpy.ndarray
Feature matrix with shape (n, 4096) with n is the number of items.
item_ids: List
List of item ids aligned with indices in `features`.
"""
features = np.load(cache(url='https://static.preferred.ai/cornac/datasets/tradesy/item_features.zip',
unzip=True, relative_path='tradesy/item_features.npy'))
item_ids = read_text(cache(url='https://static.preferred.ai/cornac/datasets/tradesy/item_ids.zip',
unzip=True, relative_path='tradesy/item_ids.txt'))
return features, item_ids