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<div class="section" id="rcv1-dataset">
<span id="rcv1"></span><h1>5.5.6. RCV1 dataset<a class="headerlink" href="#rcv1-dataset" title="Permalink to this headline">¶</a></h1>
<p>Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories made available by Reuters, Ltd. for research purposes. The dataset is extensively described in <a class="footnote-reference" href="#id3" id="id1">[1]</a>.</p>
<p><a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_rcv1.html#sklearn.datasets.fetch_rcv1" title="sklearn.datasets.fetch_rcv1"><code class="xref py py-func docutils literal"><span class="pre">sklearn.datasets.fetch_rcv1</span></code></a> will load the following version: RCV1-v2, vectors, full sets, topics multilabels:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_rcv1</span>
<span class="gp">>>> </span><span class="n">rcv1</span> <span class="o">=</span> <span class="n">fetch_rcv1</span><span class="p">()</span>
</pre></div>
</div>
<p>It returns a dictionary-like object, with the following attributes:</p>
<p><code class="docutils literal"><span class="pre">data</span></code>:
The feature matrix is a scipy CSR sparse matrix, with 804414 samples and
47236 features. Non-zero values contains cosine-normalized, log TF-IDF vectors.
A nearly chronological split is proposed in <a class="footnote-reference" href="#id3" id="id2">[1]</a>: The first 23149 samples are the training set. The last 781265 samples are the testing set. This follows the official LYRL2004 chronological split.
The array has 0.16% of non zero values:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rcv1</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(804414, 47236)</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">target</span></code>:
The target values are stored in a scipy CSR sparse matrix, with 804414 samples and 103 categories. Each sample has a value of 1 in its categories, and 0 in others. The array has 3.15% of non zero values:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rcv1</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(804414, 103)</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">sample_id</span></code>:
Each sample can be identified by its ID, ranging (with gaps) from 2286 to 810596:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rcv1</span><span class="o">.</span><span class="n">sample_id</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="go">array([2286, 2287, 2288], dtype=int32)</span>
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">target_names</span></code>:
The target values are the topics of each sample. Each sample belongs to at least one topic, and to up to 17 topics.
There are 103 topics, each represented by a string. Their corpus frequencies span five orders of magnitude, from 5 occurrences for ‘GMIL’, to 381327 for ‘CCAT’:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rcv1</span><span class="o">.</span><span class="n">target_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="go">['E11', 'ECAT', 'M11']</span>
</pre></div>
</div>
<p>The dataset will be downloaded from the <a class="reference external" href="http://jmlr.csail.mit.edu/papers/volume5/lewis04a/">rcv1 homepage</a> if necessary.
The compressed size is about 656 MB.</p>
<div class="topic">
<p class="topic-title first">References</p>
<table class="docutils footnote" frame="void" id="id3" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label">[1]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id2">2</a>)</em> 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.</td></tr>
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