335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""RCV1 dataset.
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The dataset page is available at
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http://jmlr.csail.mit.edu/papers/volume5/lewis04a/
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"""
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# Author: Tom Dupre la Tour
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# License: BSD 3 clause
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import logging
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from gzip import GzipFile
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from numbers import Integral, Real
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from os import PathLike, makedirs, remove
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from os.path import exists, join
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import joblib
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import numpy as np
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import scipy.sparse as sp
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from ..utils import Bunch
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from ..utils import shuffle as shuffle_
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from ..utils._param_validation import Interval, StrOptions, validate_params
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from . import get_data_home
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from ._base import RemoteFileMetadata, _fetch_remote, _pkl_filepath, load_descr
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from ._svmlight_format_io import load_svmlight_files
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# The original vectorized data can be found at:
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt0.dat.gz
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt1.dat.gz
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt2.dat.gz
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_test_pt3.dat.gz
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/a13-vector-files/lyrl2004_vectors_train.dat.gz
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# while the original stemmed token files can be found
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# in the README, section B.12.i.:
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# http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm
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XY_METADATA = (
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RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976069",
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checksum="ed40f7e418d10484091b059703eeb95ae3199fe042891dcec4be6696b9968374",
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filename="lyrl2004_vectors_test_pt0.dat.gz",
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),
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RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976066",
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checksum="87700668ae45d45d5ca1ef6ae9bd81ab0f5ec88cc95dcef9ae7838f727a13aa6",
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filename="lyrl2004_vectors_test_pt1.dat.gz",
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),
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RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976063",
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checksum="48143ac703cbe33299f7ae9f4995db49a258690f60e5debbff8995c34841c7f5",
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filename="lyrl2004_vectors_test_pt2.dat.gz",
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),
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RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976060",
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checksum="dfcb0d658311481523c6e6ca0c3f5a3e1d3d12cde5d7a8ce629a9006ec7dbb39",
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filename="lyrl2004_vectors_test_pt3.dat.gz",
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),
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RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976057",
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checksum="5468f656d0ba7a83afc7ad44841cf9a53048a5c083eedc005dcdb5cc768924ae",
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filename="lyrl2004_vectors_train.dat.gz",
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),
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)
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# The original data can be found at:
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# http://jmlr.csail.mit.edu/papers/volume5/lewis04a/a08-topic-qrels/rcv1-v2.topics.qrels.gz
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TOPICS_METADATA = RemoteFileMetadata(
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url="https://ndownloader.figshare.com/files/5976048",
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checksum="2a98e5e5d8b770bded93afc8930d88299474317fe14181aee1466cc754d0d1c1",
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filename="rcv1v2.topics.qrels.gz",
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)
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logger = logging.getLogger(__name__)
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@validate_params(
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{
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"data_home": [str, PathLike, None],
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"subset": [StrOptions({"train", "test", "all"})],
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"download_if_missing": ["boolean"],
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"random_state": ["random_state"],
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"shuffle": ["boolean"],
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"return_X_y": ["boolean"],
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"n_retries": [Interval(Integral, 1, None, closed="left")],
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"delay": [Interval(Real, 0.0, None, closed="neither")],
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},
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prefer_skip_nested_validation=True,
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)
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def fetch_rcv1(
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*,
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data_home=None,
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subset="all",
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download_if_missing=True,
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random_state=None,
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shuffle=False,
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return_X_y=False,
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n_retries=3,
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delay=1.0,
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):
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"""Load the RCV1 multilabel dataset (classification).
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Download it if necessary.
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Version: RCV1-v2, vectors, full sets, topics multilabels.
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================= =====================
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Classes 103
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Samples total 804414
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Dimensionality 47236
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Features real, between 0 and 1
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================= =====================
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Read more in the :ref:`User Guide <rcv1_dataset>`.
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.. versionadded:: 0.17
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Parameters
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----------
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data_home : str or path-like, default=None
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Specify another download and cache folder for the datasets. By default
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all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
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subset : {'train', 'test', 'all'}, default='all'
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Select the dataset to load: 'train' for the training set
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(23149 samples), 'test' for the test set (781265 samples),
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'all' for both, with the training samples first if shuffle is False.
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This follows the official LYRL2004 chronological split.
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download_if_missing : bool, default=True
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If False, raise an OSError if the data is not locally available
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instead of trying to download the data from the source site.
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random_state : int, RandomState instance or None, default=None
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Determines random number generation for dataset shuffling. Pass an int
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for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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shuffle : bool, default=False
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Whether to shuffle dataset.
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return_X_y : bool, default=False
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If True, returns ``(dataset.data, dataset.target)`` instead of a Bunch
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object. See below for more information about the `dataset.data` and
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`dataset.target` object.
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.. versionadded:: 0.20
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n_retries : int, default=3
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Number of retries when HTTP errors are encountered.
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.. versionadded:: 1.5
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delay : float, default=1.0
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Number of seconds between retries.
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.. versionadded:: 1.5
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Returns
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-------
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dataset : :class:`~sklearn.utils.Bunch`
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Dictionary-like object. Returned only if `return_X_y` is False.
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`dataset` has the following attributes:
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- data : sparse matrix of shape (804414, 47236), dtype=np.float64
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The array has 0.16% of non zero values. Will be of CSR format.
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- target : sparse matrix of shape (804414, 103), dtype=np.uint8
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Each sample has a value of 1 in its categories, and 0 in others.
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The array has 3.15% of non zero values. Will be of CSR format.
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- sample_id : ndarray of shape (804414,), dtype=np.uint32,
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Identification number of each sample, as ordered in dataset.data.
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- target_names : ndarray of shape (103,), dtype=object
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Names of each target (RCV1 topics), as ordered in dataset.target.
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- DESCR : str
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Description of the RCV1 dataset.
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(data, target) : tuple
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A tuple consisting of `dataset.data` and `dataset.target`, as
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described above. Returned only if `return_X_y` is True.
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.. versionadded:: 0.20
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Examples
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--------
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>>> from sklearn.datasets import fetch_rcv1
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>>> rcv1 = fetch_rcv1()
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>>> rcv1.data.shape
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(804414, 47236)
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>>> rcv1.target.shape
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(804414, 103)
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"""
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N_SAMPLES = 804414
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N_FEATURES = 47236
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N_CATEGORIES = 103
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N_TRAIN = 23149
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data_home = get_data_home(data_home=data_home)
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rcv1_dir = join(data_home, "RCV1")
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if download_if_missing:
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if not exists(rcv1_dir):
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makedirs(rcv1_dir)
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samples_path = _pkl_filepath(rcv1_dir, "samples.pkl")
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sample_id_path = _pkl_filepath(rcv1_dir, "sample_id.pkl")
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sample_topics_path = _pkl_filepath(rcv1_dir, "sample_topics.pkl")
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topics_path = _pkl_filepath(rcv1_dir, "topics_names.pkl")
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# load data (X) and sample_id
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if download_if_missing and (not exists(samples_path) or not exists(sample_id_path)):
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files = []
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for each in XY_METADATA:
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logger.info("Downloading %s" % each.url)
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file_path = _fetch_remote(
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each, dirname=rcv1_dir, n_retries=n_retries, delay=delay
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)
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files.append(GzipFile(filename=file_path))
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Xy = load_svmlight_files(files, n_features=N_FEATURES)
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# Training data is before testing data
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X = sp.vstack([Xy[8], Xy[0], Xy[2], Xy[4], Xy[6]]).tocsr()
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sample_id = np.hstack((Xy[9], Xy[1], Xy[3], Xy[5], Xy[7]))
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sample_id = sample_id.astype(np.uint32, copy=False)
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joblib.dump(X, samples_path, compress=9)
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joblib.dump(sample_id, sample_id_path, compress=9)
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# delete archives
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for f in files:
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f.close()
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remove(f.name)
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else:
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X = joblib.load(samples_path)
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sample_id = joblib.load(sample_id_path)
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# load target (y), categories, and sample_id_bis
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if download_if_missing and (
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not exists(sample_topics_path) or not exists(topics_path)
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):
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logger.info("Downloading %s" % TOPICS_METADATA.url)
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topics_archive_path = _fetch_remote(
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TOPICS_METADATA, dirname=rcv1_dir, n_retries=n_retries, delay=delay
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)
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# parse the target file
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n_cat = -1
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n_doc = -1
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doc_previous = -1
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y = np.zeros((N_SAMPLES, N_CATEGORIES), dtype=np.uint8)
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sample_id_bis = np.zeros(N_SAMPLES, dtype=np.int32)
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category_names = {}
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with GzipFile(filename=topics_archive_path, mode="rb") as f:
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for line in f:
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line_components = line.decode("ascii").split(" ")
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if len(line_components) == 3:
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cat, doc, _ = line_components
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if cat not in category_names:
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n_cat += 1
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category_names[cat] = n_cat
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doc = int(doc)
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if doc != doc_previous:
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doc_previous = doc
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n_doc += 1
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sample_id_bis[n_doc] = doc
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y[n_doc, category_names[cat]] = 1
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# delete archive
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remove(topics_archive_path)
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# Samples in X are ordered with sample_id,
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# whereas in y, they are ordered with sample_id_bis.
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permutation = _find_permutation(sample_id_bis, sample_id)
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y = y[permutation, :]
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# save category names in a list, with same order than y
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categories = np.empty(N_CATEGORIES, dtype=object)
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for k in category_names.keys():
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categories[category_names[k]] = k
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# reorder categories in lexicographic order
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order = np.argsort(categories)
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categories = categories[order]
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y = sp.csr_matrix(y[:, order])
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joblib.dump(y, sample_topics_path, compress=9)
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joblib.dump(categories, topics_path, compress=9)
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else:
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y = joblib.load(sample_topics_path)
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categories = joblib.load(topics_path)
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if subset == "all":
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pass
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elif subset == "train":
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X = X[:N_TRAIN, :]
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y = y[:N_TRAIN, :]
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sample_id = sample_id[:N_TRAIN]
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elif subset == "test":
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X = X[N_TRAIN:, :]
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y = y[N_TRAIN:, :]
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sample_id = sample_id[N_TRAIN:]
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else:
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raise ValueError(
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"Unknown subset parameter. Got '%s' instead of one"
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" of ('all', 'train', test')" % subset
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)
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if shuffle:
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X, y, sample_id = shuffle_(X, y, sample_id, random_state=random_state)
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fdescr = load_descr("rcv1.rst")
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if return_X_y:
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return X, y
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return Bunch(
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data=X, target=y, sample_id=sample_id, target_names=categories, DESCR=fdescr
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)
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def _inverse_permutation(p):
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"""Inverse permutation p."""
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n = p.size
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s = np.zeros(n, dtype=np.int32)
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i = np.arange(n, dtype=np.int32)
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np.put(s, p, i) # s[p] = i
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return s
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def _find_permutation(a, b):
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"""Find the permutation from a to b."""
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t = np.argsort(a)
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u = np.argsort(b)
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u_ = _inverse_permutation(u)
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return t[u_]
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