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<ul>
<li><a class="reference internal" href="#">Downloading datasets from the mldata.org repository</a></li>
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<div class="section" id="downloading-datasets-from-the-mldata-org-repository">
<span id="mldata"></span><h1>Downloading datasets from the mldata.org repository<a class="headerlink" href="#downloading-datasets-from-the-mldata-org-repository" title="Permalink to this headline">¶</a></h1>
<p><a class="reference external" href="http://mldata.org">mldata.org</a> is a public repository for machine learning
data, supported by the <a class="reference external" href="http://www.pascal-network.org">PASCAL network</a> .</p>
<p>The <tt class="docutils literal"><span class="pre">sklearn.datasets</span></tt> package is able to directly download data
sets from the repository using the function
<a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_mldata.html#sklearn.datasets.fetch_mldata" title="sklearn.datasets.fetch_mldata"><tt class="xref py py-func docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></tt></a>.</p>
<p>For example, to download the MNIST digit recognition database:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_mldata</span>
<span class="gp">>>> </span><span class="n">mnist</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s">'MNIST original'</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="n">custom_data_home</span><span class="p">)</span>
</pre></div>
</div>
<p>The MNIST database contains a total of 70000 examples of handwritten digits
of size 28x28 pixels, labeled from 0 to 9:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">mnist</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(70000, 784)</span>
<span class="gp">>>> </span><span class="n">mnist</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(70000,)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">mnist</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="go">array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])</span>
</pre></div>
</div>
<p>After the first download, the dataset is cached locally in the path
specified by the <tt class="docutils literal"><span class="pre">data_home</span></tt> keyword argument, which defaults to
<tt class="docutils literal"><span class="pre">~/scikit_learn_data/</span></tt>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">custom_data_home</span><span class="p">,</span> <span class="s">'mldata'</span><span class="p">))</span>
<span class="go">['mnist-original.mat']</span>
</pre></div>
</div>
<p>Data sets in <a class="reference external" href="http://mldata.org">mldata.org</a> do not adhere to a strict
naming or formatting convention. <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_mldata.html#sklearn.datasets.fetch_mldata" title="sklearn.datasets.fetch_mldata"><tt class="xref py py-func docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></tt></a> is
able to make sense of the most common cases, but allows to tailor the
defaults to individual datasets:</p>
<ul>
<li><p class="first">The data arrays in <a class="reference external" href="http://mldata.org">mldata.org</a> are most often
shaped as <tt class="docutils literal"><span class="pre">(n_features,</span> <span class="pre">n_samples)</span></tt>. This is the opposite of the
<tt class="docutils literal"><span class="pre">scikit-learn</span></tt> convention, so <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_mldata.html#sklearn.datasets.fetch_mldata" title="sklearn.datasets.fetch_mldata"><tt class="xref py py-func docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></tt></a>
transposes the matrix by default. The <tt class="docutils literal"><span class="pre">transpose_data</span></tt> keyword controls
this behavior:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s">'iris'</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="n">custom_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(150, 4)</span>
<span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s">'iris'</span><span class="p">,</span> <span class="n">transpose_data</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_home</span><span class="o">=</span><span class="n">custom_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(4, 150)</span>
</pre></div>
</div>
</li>
<li><p class="first">For datasets with multiple columns, <a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_mldata.html#sklearn.datasets.fetch_mldata" title="sklearn.datasets.fetch_mldata"><tt class="xref py py-func docutils literal"><span class="pre">sklearn.datasets.fetch_mldata</span></tt></a>
tries to identify the target and data columns and rename them to <tt class="docutils literal"><span class="pre">target</span></tt>
and <tt class="docutils literal"><span class="pre">data</span></tt>. This is done by looking for arrays named <tt class="docutils literal"><span class="pre">label</span></tt> and
<tt class="docutils literal"><span class="pre">data</span></tt> in the dataset, and failing that by choosing the first array to be
<tt class="docutils literal"><span class="pre">target</span></tt> and the second to be <tt class="docutils literal"><span class="pre">data</span></tt>. This behavior can be changed with
the <tt class="docutils literal"><span class="pre">target_name</span></tt> and <tt class="docutils literal"><span class="pre">data_name</span></tt> keywords, setting them to a specific
name or index number (the name and order of the columns in the datasets
can be found at its <a class="reference external" href="http://mldata.org">mldata.org</a> under the tab “Data”:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="n">iris2</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s">'datasets-UCI iris'</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">data_name</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_home</span><span class="o">=</span><span class="n">custom_data_home</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris3</span> <span class="o">=</span> <span class="n">fetch_mldata</span><span class="p">(</span><span class="s">'datasets-UCI iris'</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="s">'class'</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">data_name</span><span class="o">=</span><span class="s">'double0'</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="n">custom_data_home</span><span class="p">)</span>
</pre></div>
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</li>
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