Mathieu Blondel

Amazon7

Amazon7 is a dataset that we created from raw data collected by Mark Dredze and his colleagues. The dataset contains 1,362,109 reviews of Amazon products. Each review may belong to one of 7 categories (apparel, book, dvd, electronics, kitchen & housewares, music, video) and is represented as a 262,144 dimensional vector. The dataset contains 0.04% non-zero features. This is the dataset that we used in our paper "Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass Classification" (see below).

pickle

amazon7_pkl.tar.bz2 (303 MB download)

For Python users, the easiest way to load the dataset is to use joblib's pickle functionalities. After decompressing the above archive, you can load the dataset as follows:

import joblib

data = joblib.load("amazon7_pkl/amazon7.pkl")
X = data["X"]
y = data["y"]
print(X.shape)
print(y.shape)
print(data["categories"])

Alternatively, you may want to use:

amazon7_uncompressed_pkl.tar.bz2 (204 MB download, 1.5 GB uncompressed)

The advantage is that the data can be loaded with mmap:

data = joblib.load("amazon7_uncompressed_pkl/amazon7.pkl", mmap_mode="r")

This is much faster, since data is not copied to memory.

svmlight / libsvm format

amazon7.bz2 [209 MB]

For convenience, the dataset is also provided in the well-known svmlight / libsvm format.

Citation

If you use Amazon7 in a paper, please cite both the following papers.