"""RCV1 dataset. The dataset page is available at http://jmlr.csail.mit.edu/papers/volume5/lewis04a/ """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import logging from gzip import GzipFile from numbers import Integral, Real 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 Interval, 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"], "n_retries": [Interval(Integral, 1, None, closed="left")], "delay": [Interval(Real, 0.0, None, closed="neither")], }, 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, n_retries=3, delay=1.0, ): """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 `. .. 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 `. 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 n_retries : int, default=3 Number of retries when HTTP errors are encountered. .. versionadded:: 1.5 delay : float, default=1.0 Number of seconds between retries. .. versionadded:: 1.5 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 Examples -------- >>> from sklearn.datasets import fetch_rcv1 >>> rcv1 = fetch_rcv1() >>> rcv1.data.shape (804414, 47236) >>> rcv1.target.shape (804414, 103) """ 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, n_retries=n_retries, delay=delay ) 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, n_retries=n_retries, delay=delay ) # 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_]