307 lines
11 KiB
Python
307 lines
11 KiB
Python
|
"""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 gzip import GzipFile
|
||
|
|
from os import PathLike, makedirs, remove
|
||
|
|
from os.path import exists, join
|
||
|
|
|
||
|
|
import joblib
|
||
|
|
import numpy as np
|
||
|
|
import scipy.sparse as sp
|
||
|
|
|
||
|
|
from ..utils import Bunch
|
||
|
|
from ..utils import shuffle as shuffle_
|
||
|
|
from ..utils._param_validation import StrOptions, validate_params
|
||
|
|
from . import get_data_home
|
||
|
|
from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr
|
||
|
|
from ._svmlight_format_io import load_svmlight_files
|
||
|
|
|
||
|
|
# 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__)
|
||
|
|
|
||
|
|
|
||
|
|
@validate_params(
|
||
|
|
{
|
||
|
|
"data_home": [str, PathLike, None],
|
||
|
|
"subset": [StrOptions({"train", "test", "all"})],
|
||
|
|
"download_if_missing": ["boolean"],
|
||
|
|
"random_state": ["random_state"],
|
||
|
|
"shuffle": ["boolean"],
|
||
|
|
"return_X_y": ["boolean"],
|
||
|
|
},
|
||
|
|
prefer_skip_nested_validation=True,
|
||
|
|
)
|
||
|
|
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 or path-like, 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 an OSError 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_]
|