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"""RCV1 dataset.
The dataset page is available at
http://jmlr.csail.mit.edu/papers/volume5/lewis04a/
"""
# Author: Tom Dupre la Tour
# License: BSD 3 clause
import logging
from os import remove, makedirs
from os.path import exists, join
from gzip import GzipFile
import numpy as np
import scipy.sparse as sp
import joblib
from . import get_data_home
from ._base import _pkl_filepath
from ._base import _fetch_remote
from ._base import RemoteFileMetadata
from ._base import load_descr
from ._svmlight_format_io import load_svmlight_files
from ..utils import shuffle as shuffle_
from ..utils import Bunch
# The original vectorized data can be found at:
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt0.dat.gz
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt1.dat.gz
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt2.dat.gz
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt3.dat.gz
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_train.dat.gz
# while the original stemmed token files can be found
# in the README, section B.12.i.:
# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm
XY_METADATA = (
RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976069",
checksum="ed40f7e418d10484091b059703eeb95ae3199fe042891dcec4be6696b9968374",
filename="lyrl2004_vectors_test_pt0.dat.gz",
),
RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976066",
checksum="87700668ae45d45d5ca1ef6ae9bd81ab0f5ec88cc95dcef9ae7838f727a13aa6",
filename="lyrl2004_vectors_test_pt1.dat.gz",
),
RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976063",
checksum="48143ac703cbe33299f7ae9f4995db49a258690f60e5debbff8995c34841c7f5",
filename="lyrl2004_vectors_test_pt2.dat.gz",
),
RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976060",
checksum="dfcb0d658311481523c6e6ca0c3f5a3e1d3d12cde5d7a8ce629a9006ec7dbb39",
filename="lyrl2004_vectors_test_pt3.dat.gz",
),
RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976057",
checksum="5468f656d0ba7a83afc7ad44841cf9a53048a5c083eedc005dcdb5cc768924ae",
filename="lyrl2004_vectors_train.dat.gz",
),
)
# The original data can be found at:
# http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a08-topic-qrels/rcv1-v2.topics.qrels.gz
TOPICS_METADATA = RemoteFileMetadata(
url="https://ndownloader.figshare.com/files/5976048",
checksum="2a98e5e5d8b770bded93afc8930d88299474317fe14181aee1466cc754d0d1c1",
filename="rcv1v2.topics.qrels.gz",
)
logger = logging.getLogger(__name__)
def fetch_rcv1(
*,
data_home=None,
subset="all",
download_if_missing=True,
random_state=None,
shuffle=False,
return_X_y=False,
):
"""Load the RCV1 multilabel dataset (classification).
Download it if necessary.
Version: RCV1-v2, vectors, full sets, topics multilabels.
================= =====================
Classes 103
Samples total 804414
Dimensionality 47236
Features real, between 0 and 1
================= =====================
Read more in the :ref:`User Guide <rcv1_dataset>`.
.. versionadded:: 0.17
Parameters
----------
data_home : str, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
subset : {'train', 'test', 'all'}, default='all'
Select the dataset to load: 'train' for the training set
(23149 samples), 'test' for the test set (781265 samples),
'all' for both, with the training samples first if shuffle is False.
This follows the official LYRL2004 chronological split.
download_if_missing : bool, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
random_state : int, RandomState instance or None, default=None
Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
shuffle : bool, default=False
Whether to shuffle dataset.
return_X_y : bool, default=False
If True, returns ``(dataset.data, dataset.target)`` instead of a Bunch
object. See below for more information about the `dataset.data` and
`dataset.target` object.
.. versionadded:: 0.20
Returns
-------
dataset : :class:`~sklearn.utils.Bunch`
Dictionary-like object. Returned only if `return_X_y` is False.
`dataset` has the following attributes:
- data : sparse matrix of shape (804414, 47236), dtype=np.float64
The array has 0.16% of non zero values. Will be of CSR format.
- target : sparse matrix of shape (804414, 103), dtype=np.uint8
Each sample has a value of 1 in its categories, and 0 in others.
The array has 3.15% of non zero values. Will be of CSR format.
- sample_id : ndarray of shape (804414,), dtype=np.uint32,
Identification number of each sample, as ordered in dataset.data.
- target_names : ndarray of shape (103,), dtype=object
Names of each target (RCV1 topics), as ordered in dataset.target.
- DESCR : str
Description of the RCV1 dataset.
(data, target) : tuple
A tuple consisting of `dataset.data` and `dataset.target`, as
described above. Returned only if `return_X_y` is True.
.. versionadded:: 0.20
"""
N_SAMPLES = 804414
N_FEATURES = 47236
N_CATEGORIES = 103
N_TRAIN = 23149
data_home = get_data_home(data_home=data_home)
rcv1_dir = join(data_home, "RCV1")
if download_if_missing:
if not exists(rcv1_dir):
makedirs(rcv1_dir)
samples_path = _pkl_filepath(rcv1_dir, "samples.pkl")
sample_id_path = _pkl_filepath(rcv1_dir, "sample_id.pkl")
sample_topics_path = _pkl_filepath(rcv1_dir, "sample_topics.pkl")
topics_path = _pkl_filepath(rcv1_dir, "topics_names.pkl")
# load data (X) and sample_id
if download_if_missing and (not exists(samples_path) or not exists(sample_id_path)):
files = []
for each in XY_METADATA:
logger.info("Downloading %s" % each.url)
file_path = _fetch_remote(each, dirname=rcv1_dir)
files.append(GzipFile(filename=file_path))
Xy = load_svmlight_files(files, n_features=N_FEATURES)
# Training data is before testing data
X = sp.vstack([Xy[8], Xy[0], Xy[2], Xy[4], Xy[6]]).tocsr()
sample_id = np.hstack((Xy[9], Xy[1], Xy[3], Xy[5], Xy[7]))
sample_id = sample_id.astype(np.uint32, copy=False)
joblib.dump(X, samples_path, compress=9)
joblib.dump(sample_id, sample_id_path, compress=9)
# delete archives
for f in files:
f.close()
remove(f.name)
else:
X = joblib.load(samples_path)
sample_id = joblib.load(sample_id_path)
# load target (y), categories, and sample_id_bis
if download_if_missing and (
not exists(sample_topics_path) or not exists(topics_path)
):
logger.info("Downloading %s" % TOPICS_METADATA.url)
topics_archive_path = _fetch_remote(TOPICS_METADATA, dirname=rcv1_dir)
# parse the target file
n_cat = -1
n_doc = -1
doc_previous = -1
y = np.zeros((N_SAMPLES, N_CATEGORIES), dtype=np.uint8)
sample_id_bis = np.zeros(N_SAMPLES, dtype=np.int32)
category_names = {}
with GzipFile(filename=topics_archive_path, mode="rb") as f:
for line in f:
line_components = line.decode("ascii").split(" ")
if len(line_components) == 3:
cat, doc, _ = line_components
if cat not in category_names:
n_cat += 1
category_names[cat] = n_cat
doc = int(doc)
if doc != doc_previous:
doc_previous = doc
n_doc += 1
sample_id_bis[n_doc] = doc
y[n_doc, category_names[cat]] = 1
# delete archive
remove(topics_archive_path)
# Samples in X are ordered with sample_id,
# whereas in y, they are ordered with sample_id_bis.
permutation = _find_permutation(sample_id_bis, sample_id)
y = y[permutation, :]
# save category names in a list, with same order than y
categories = np.empty(N_CATEGORIES, dtype=object)
for k in category_names.keys():
categories[category_names[k]] = k
# reorder categories in lexicographic order
order = np.argsort(categories)
categories = categories[order]
y = sp.csr_matrix(y[:, order])
joblib.dump(y, sample_topics_path, compress=9)
joblib.dump(categories, topics_path, compress=9)
else:
y = joblib.load(sample_topics_path)
categories = joblib.load(topics_path)
if subset == "all":
pass
elif subset == "train":
X = X[:N_TRAIN, :]
y = y[:N_TRAIN, :]
sample_id = sample_id[:N_TRAIN]
elif subset == "test":
X = X[N_TRAIN:, :]
y = y[N_TRAIN:, :]
sample_id = sample_id[N_TRAIN:]
else:
raise ValueError(
"Unknown subset parameter. Got '%s' instead of one"
" of ('all', 'train', test')" % subset
)
if shuffle:
X, y, sample_id = shuffle_(X, y, sample_id, random_state=random_state)
fdescr = load_descr("rcv1.rst")
if return_X_y:
return X, y
return Bunch(
data=X, target=y, sample_id=sample_id, target_names=categories, DESCR=fdescr
)
def _inverse_permutation(p):
"""Inverse permutation p."""
n = p.size
s = np.zeros(n, dtype=np.int32)
i = np.arange(n, dtype=np.int32)
np.put(s, p, i) # s[p] = i
return s
def _find_permutation(a, b):
"""Find the permutation from a to b."""
t = np.argsort(a)
u = np.argsort(b)
u_ = _inverse_permutation(u)
return t[u_]