code: 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 <datasets>`.
  
      .. 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(" ")
              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__,
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/datasets/rcv1.py
imports: import logging
  from os.path import exists, join
  from gzip import GzipFile
  from io import BytesIO
  from contextlib import closing
  import numpy
  import scipy.sparse
  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
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_rcv1(data_home, subset, download_if_missing, random_state, shuffle) and returns: Bunch