RCV1 dataset

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories made available by Reuters, Ltd. for research purposes. The dataset is extensively described in [1].

Data Set Characteristics:

Classes	103
Samples total	804414
Dimensionality	47236
Features	real, between 0 and 1

:func:`sklearn.datasets.fetch_rcv1` will load the following version: RCV1-v2, vectors, full sets, topics multilabels:

>>> from sklearn.datasets import fetch_rcv1
>>> rcv1 = fetch_rcv1()

It returns a dictionary-like object, with the following attributes:

data: The feature matrix is a scipy CSR sparse matrix, with 804414 samples and 47236 features. Non-zero values contains cosine-normalized, log TF-IDF vectors. A nearly chronological split is proposed in [1]: The first 23149 samples are the training set. The last 781265 samples are the testing set. This follows the official LYRL2004 chronological split. The array has 0.16% of non zero values:

>>> rcv1.data.shape
(804414, 47236)

target: The target values are stored in a scipy CSR sparse matrix, with 804414 samples and 103 categories. Each sample has a value of 1 in its categories, and 0 in others. The array has 3.15% of non zero values:

>>> rcv1.target.shape
(804414, 103)

sample_id: Each sample can be identified by its ID, ranging (with gaps) from 2286 to 810596:

>>> rcv1.sample_id[:3]
array([2286, 2287, 2288], dtype=uint32)

target_names: The target values are the topics of each sample. Each sample belongs to at least one topic, and to up to 17 topics. There are 103 topics, each represented by a string. Their corpus frequencies span five orders of magnitude, from 5 occurrences for 'GMIL', to 381327 for 'CCAT':

>>> rcv1.target_names[:3].tolist()  # doctest: +SKIP
['E11', 'ECAT', 'M11']

The dataset will be downloaded from the rcv1 homepage if necessary. The compressed size is about 656 MB.