"""RCV1 dataset. """ # Author: Tom Dupre la Tour # License: BSD 3 clause import logging from os.path import exists, join from gzip import GzipFile from io import BytesIO from contextlib import closing try: from urllib2 import urlopen except ImportError: from urllib.request import urlopen import numpy as np import scipy.sparse as sp from .base import get_data_home from .base import Bunch from .base import _pkl_filepath from ..utils.fixes import makedirs from ..externals import joblib from .svmlight_format import load_svmlight_files from ..utils import shuffle as shuffle_ URL = ('http://jmlr.csail.mit.edu/papers/volume5/lewis04a/' 'a13-vector-files/lyrl2004_vectors') URL_topics = ('http://jmlr.csail.mit.edu/papers/volume5/lewis04a/' 'a08-topic-qrels/rcv1-v2.topics.qrels.gz') logger = logging.getLogger() def fetch_rcv1(data_home=None, subset='all', download_if_missing=True, random_state=None, shuffle=False): """Load the RCV1 multilabel dataset, downloading 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 : string, optional Specify another download and cache folder for the datasets. By default all scikit learn data is stored in '~/scikit_learn_data' subfolders. subset: string, 'train', 'test', or '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 : boolean, 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, optional (default=None) Random state for shuffling the dataset. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. shuffle : bool, default=False Whether to shuffle dataset. Returns ------- dataset : dict-like object with the following attributes: dataset.data : scipy csr array, dtype np.float64, shape (804414, 47236) The array has 0.16% of non zero values. dataset.target : scipy csr array, dtype np.uint8, shape (804414, 103) Each sample has a value of 1 in its categories, and 0 in others. The array has 3.15% of non zero values. dataset.sample_id : numpy array, dtype np.uint32, shape (804414,) Identification number of each sample, as ordered in dataset.data. dataset.target_names : numpy array, dtype object, length (103) Names of each target (RCV1 topics), as ordered in dataset.target. dataset.DESCR : string Description of the RCV1 dataset. References ---------- Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5, 361-397. """ 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: makedirs(rcv1_dir, exist_ok=True) 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)): file_urls = ["%s_test_pt%d.dat.gz" % (URL, i) for i in range(4)] file_urls.append("%s_train.dat.gz" % URL) files = [] for file_url in file_urls: logger.warning("Downloading %s" % file_url) with closing(urlopen(file_url)) as online_file: # buffer the full file in memory to make possible to Gzip to # work correctly f = BytesIO(online_file.read()) files.append(GzipFile(fileobj=f)) 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) joblib.dump(X, samples_path, compress=9) joblib.dump(sample_id, sample_id_path, compress=9) 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.warning("Downloading %s" % URL_topics) with closing(urlopen(URL_topics)) as online_topics: f = BytesIO(online_topics.read()) # 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 = {} for line in GzipFile(fileobj=f, mode='rb'): line_components = line.decode("ascii").split(u" ") 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 # 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) return Bunch(data=X, target=y, sample_id=sample_id, target_names=categories, DESCR=__doc__) 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_]