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<h1 class="title">Data sources</h1>
<p>In this section, we introduce how to use data source in ML to load data.
Besides some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.</p>
<p><strong>Table of Contents</strong></p>
<ul id="markdown-toc">
<li><a href="#image-data-source" id="markdown-toc-image-data-source">Image data source</a></li>
<li><a href="#libsvm-data-source" id="markdown-toc-libsvm-data-source">LIBSVM data source</a></li>
</ul>
<h2 id="image-data-source">Image data source</h2>
<p>This image data source is used to load image files from a directory, it can load compressed image (jpeg, png, etc.) into raw image representation via <code class="language-plaintext highlighter-rouge">ImageIO</code> in Java library.
The loaded DataFrame has one <code class="language-plaintext highlighter-rouge">StructType</code> column: &#8220;image&#8221;, containing image data stored as image schema.
The schema of the <code class="language-plaintext highlighter-rouge">image</code> column is:</p>
<ul>
<li>origin: <code class="language-plaintext highlighter-rouge">StringType</code> (represents the file path of the image)</li>
<li>height: <code class="language-plaintext highlighter-rouge">IntegerType</code> (height of the image)</li>
<li>width: <code class="language-plaintext highlighter-rouge">IntegerType</code> (width of the image)</li>
<li>nChannels: <code class="language-plaintext highlighter-rouge">IntegerType</code> (number of image channels)</li>
<li>mode: <code class="language-plaintext highlighter-rouge">IntegerType</code> (OpenCV-compatible type)</li>
<li>data: <code class="language-plaintext highlighter-rouge">BinaryType</code> (Image bytes in OpenCV-compatible order: row-wise BGR in most cases)</li>
</ul>
<div class="codetabs">
<div data-lang="python">
<p>In PySpark we provide Spark SQL data source API for loading image data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="o">&gt;&gt;&gt;</span> <span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="s">"image"</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">"dropInvalid"</span><span class="p">,</span> <span class="bp">True</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">"data/mllib/images/origin/kittens"</span><span class="p">)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">df</span><span class="p">.</span><span class="n">select</span><span class="p">(</span><span class="s">"image.origin"</span><span class="p">,</span> <span class="s">"image.width"</span><span class="p">,</span> <span class="s">"image.height"</span><span class="p">).</span><span class="n">show</span><span class="p">(</span><span class="n">truncate</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span>
<span class="o">|</span><span class="n">origin</span> <span class="o">|</span><span class="n">width</span><span class="o">|</span><span class="n">height</span><span class="o">|</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span>
<span class="o">|</span><span class="nb">file</span><span class="p">:</span><span class="o">///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="mf">54893.j</span><span class="n">pg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">311</span> <span class="o">|</span>
<span class="o">|</span><span class="nb">file</span><span class="p">:</span><span class="o">///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="n">DP802813</span><span class="p">.</span><span class="n">jpg</span> <span class="o">|</span><span class="mi">199</span> <span class="o">|</span><span class="mi">313</span> <span class="o">|</span>
<span class="o">|</span><span class="nb">file</span><span class="p">:</span><span class="o">///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="mf">29.5</span><span class="p">.</span><span class="n">a_b_EGDP022204</span><span class="p">.</span><span class="n">jpg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">200</span> <span class="o">|</span>
<span class="o">|</span><span class="nb">file</span><span class="p">:</span><span class="o">///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="n">DP153539</span><span class="p">.</span><span class="n">jpg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">296</span> <span class="o">|</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span></code></pre></figure>
</div>
<div data-lang="scala">
<p><a href="api/scala/org/apache/spark/ml/source/image/ImageDataSource.html"><code class="language-plaintext highlighter-rouge">ImageDataSource</code></a>
implements a Spark SQL data source API for loading image data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="n">scala</span><span class="o">&gt;</span> <span class="k">val</span> <span class="nv">df</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">read</span><span class="o">.</span><span class="py">format</span><span class="o">(</span><span class="s">"image"</span><span class="o">).</span><span class="py">option</span><span class="o">(</span><span class="s">"dropInvalid"</span><span class="o">,</span> <span class="kc">true</span><span class="o">).</span><span class="py">load</span><span class="o">(</span><span class="s">"data/mllib/images/origin/kittens"</span><span class="o">)</span>
<span class="n">df</span><span class="k">:</span> <span class="kt">org.apache.spark.sql.DataFrame</span> <span class="o">=</span> <span class="o">[</span><span class="kt">image:</span> <span class="kt">struct&lt;origin:</span> <span class="kt">string</span>, <span class="kt">height:</span> <span class="kt">int</span> <span class="kt">...</span> <span class="err">4</span> <span class="kt">more</span> <span class="kt">fields&gt;</span><span class="o">]</span>
<span class="n">scala</span><span class="o">&gt;</span> <span class="nv">df</span><span class="o">.</span><span class="py">select</span><span class="o">(</span><span class="s">"image.origin"</span><span class="o">,</span> <span class="s">"image.width"</span><span class="o">,</span> <span class="s">"image.height"</span><span class="o">).</span><span class="py">show</span><span class="o">(</span><span class="n">truncate</span><span class="k">=</span><span class="kc">false</span><span class="o">)</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span>
<span class="o">|</span><span class="n">origin</span> <span class="o">|</span><span class="n">width</span><span class="o">|</span><span class="n">height</span><span class="o">|</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span>
<span class="o">|</span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="mf">54893.</span><span class="n">jpg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">311</span> <span class="o">|</span>
<span class="o">|</span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="nv">DP802813</span><span class="o">.</span><span class="py">jpg</span> <span class="o">|</span><span class="mi">199</span> <span class="o">|</span><span class="mi">313</span> <span class="o">|</span>
<span class="o">|</span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="mf">29.5</span><span class="o">.</span><span class="py">a_b_EGDP022204</span><span class="o">.</span><span class="py">jpg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">200</span> <span class="o">|</span>
<span class="o">|</span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="nv">DP153539</span><span class="o">.</span><span class="py">jpg</span> <span class="o">|</span><span class="mi">300</span> <span class="o">|</span><span class="mi">296</span> <span class="o">|</span>
<span class="o">+-----------------------------------------------------------------------+-----+------+</span></code></pre></figure>
</div>
<div data-lang="java">
<p><a href="api/java/org/apache/spark/ml/source/image/ImageDataSource.html"><code class="language-plaintext highlighter-rouge">ImageDataSource</code></a>
implements Spark SQL data source API for loading image data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="nc">Dataset</span><span class="o">&lt;</span><span class="nc">Row</span><span class="o">&gt;</span> <span class="n">imagesDF</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">read</span><span class="o">().</span><span class="na">format</span><span class="o">(</span><span class="s">"image"</span><span class="o">).</span><span class="na">option</span><span class="o">(</span><span class="s">"dropInvalid"</span><span class="o">,</span> <span class="kc">true</span><span class="o">).</span><span class="na">load</span><span class="o">(</span><span class="s">"data/mllib/images/origin/kittens"</span><span class="o">);</span>
<span class="n">imageDF</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"image.origin"</span><span class="o">,</span> <span class="s">"image.width"</span><span class="o">,</span> <span class="s">"image.height"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="kc">false</span><span class="o">);</span>
<span class="cm">/*
Will output:
+-----------------------------------------------------------------------+-----+------+
|origin |width|height|
+-----------------------------------------------------------------------+-----+------+
|file:///spark/data/mllib/images/origin/kittens/54893.jpg |300 |311 |
|file:///spark/data/mllib/images/origin/kittens/DP802813.jpg |199 |313 |
|file:///spark/data/mllib/images/origin/kittens/29.5.a_b_EGDP022204.jpg |300 |200 |
|file:///spark/data/mllib/images/origin/kittens/DP153539.jpg |300 |296 |
+-----------------------------------------------------------------------+-----+------+
*/</span></code></pre></figure>
</div>
<div data-lang="r">
<p>In SparkR we provide Spark SQL data source API for loading image data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="s2">"data/mllib/images/origin/kittens"</span><span class="p">,</span><span class="w"> </span><span class="s2">"image"</span><span class="p">)</span><span class="w">
</span><span class="o">&gt;</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">image.origin</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">image.width</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">image.height</span><span class="p">))</span><span class="w">
</span><span class="m">1</span><span class="w"> </span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="m">54893</span><span class="n">.jpg</span><span class="w">
</span><span class="m">2</span><span class="w"> </span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="n">DP802813.jpg</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="m">29.5</span><span class="n">.a_b_EGDP022204.jpg</span><span class="w">
</span><span class="m">4</span><span class="w"> </span><span class="n">file</span><span class="o">:///</span><span class="n">spark</span><span class="o">/</span><span class="n">data</span><span class="o">/</span><span class="n">mllib</span><span class="o">/</span><span class="n">images</span><span class="o">/</span><span class="n">origin</span><span class="o">/</span><span class="n">kittens</span><span class="o">/</span><span class="n">DP153539.jpg</span><span class="w">
</span><span class="n">width</span><span class="w"> </span><span class="n">height</span><span class="w">
</span><span class="m">1</span><span class="w"> </span><span class="m">300</span><span class="w"> </span><span class="m">311</span><span class="w">
</span><span class="m">2</span><span class="w"> </span><span class="m">199</span><span class="w"> </span><span class="m">313</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="m">300</span><span class="w"> </span><span class="m">200</span><span class="w">
</span><span class="m">4</span><span class="w"> </span><span class="m">300</span><span class="w"> </span><span class="m">296</span></code></pre></figure>
</div>
</div>
<h2 id="libsvm-data-source">LIBSVM data source</h2>
<p>This <code class="language-plaintext highlighter-rouge">LIBSVM</code> data source is used to load &#8216;libsvm&#8217; type files from a directory.
The loaded DataFrame has two columns: label containing labels stored as doubles and features containing feature vectors stored as Vectors.
The schemas of the columns are:</p>
<ul>
<li>label: <code class="language-plaintext highlighter-rouge">DoubleType</code> (represents the instance label)</li>
<li>features: <code class="language-plaintext highlighter-rouge">VectorUDT</code> (represents the feature vector)</li>
</ul>
<div class="codetabs">
<div data-lang="python">
<p>In PySpark we provide Spark SQL data source API for loading <code class="language-plaintext highlighter-rouge">LIBSVM</code> data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-python" data-lang="python"><span class="o">&gt;&gt;&gt;</span> <span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="p">.</span><span class="n">read</span><span class="p">.</span><span class="nb">format</span><span class="p">(</span><span class="s">"libsvm"</span><span class="p">).</span><span class="n">option</span><span class="p">(</span><span class="s">"numFeatures"</span><span class="p">,</span> <span class="s">"780"</span><span class="p">).</span><span class="n">load</span><span class="p">(</span><span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span>
<span class="o">&gt;&gt;&gt;</span> <span class="n">df</span><span class="p">.</span><span class="n">show</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="o">+-----+--------------------+</span>
<span class="o">|</span><span class="n">label</span><span class="o">|</span> <span class="n">features</span><span class="o">|</span>
<span class="o">+-----+--------------------+</span>
<span class="o">|</span> <span class="mf">0.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">127</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mf">129.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">158</span><span class="p">,</span><span class="mi">159</span><span class="p">,</span><span class="mf">160.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">124</span><span class="p">,</span><span class="mi">125</span><span class="p">,</span><span class="mf">126.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">152</span><span class="p">,</span><span class="mi">153</span><span class="p">,</span><span class="mf">154.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">151</span><span class="p">,</span><span class="mi">152</span><span class="p">,</span><span class="mf">153.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">0.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">129</span><span class="p">,</span><span class="mi">130</span><span class="p">,</span><span class="mf">131.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">158</span><span class="p">,</span><span class="mi">159</span><span class="p">,</span><span class="mf">160.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">1.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">99</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">101</span><span class="p">,...</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">0.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">154</span><span class="p">,</span><span class="mi">155</span><span class="p">,</span><span class="mf">156.</span><span class="p">..</span><span class="o">|</span>
<span class="o">|</span> <span class="mf">0.0</span><span class="o">|</span><span class="p">(</span><span class="mi">780</span><span class="p">,[</span><span class="mi">127</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mf">129.</span><span class="p">..</span><span class="o">|</span>
<span class="o">+-----+--------------------+</span>
<span class="n">only</span> <span class="n">showing</span> <span class="n">top</span> <span class="mi">10</span> <span class="n">rows</span></code></pre></figure>
</div>
<div data-lang="scala">
<p><a href="api/scala/org/apache/spark/ml/source/libsvm/LibSVMDataSource.html"><code class="language-plaintext highlighter-rouge">LibSVMDataSource</code></a>
implements a Spark SQL data source API for loading <code class="language-plaintext highlighter-rouge">LIBSVM</code> data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="n">scala</span><span class="o">&gt;</span> <span class="k">val</span> <span class="nv">df</span> <span class="k">=</span> <span class="nv">spark</span><span class="o">.</span><span class="py">read</span><span class="o">.</span><span class="py">format</span><span class="o">(</span><span class="s">"libsvm"</span><span class="o">).</span><span class="py">option</span><span class="o">(</span><span class="s">"numFeatures"</span><span class="o">,</span> <span class="s">"780"</span><span class="o">).</span><span class="py">load</span><span class="o">(</span><span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">)</span>
<span class="n">df</span><span class="k">:</span> <span class="kt">org.apache.spark.sql.DataFrame</span> <span class="o">=</span> <span class="o">[</span><span class="kt">label:</span> <span class="kt">double</span>, <span class="kt">features:</span> <span class="kt">vector</span><span class="o">]</span>
<span class="n">scala</span><span class="o">&gt;</span> <span class="nv">df</span><span class="o">.</span><span class="py">show</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">+-----+--------------------+</span>
<span class="o">|</span><span class="n">label</span><span class="o">|</span> <span class="n">features</span><span class="o">|</span>
<span class="o">+-----+--------------------+</span>
<span class="o">|</span> <span class="mf">0.0</span><span class="o">|(</span><span class="mi">780</span><span class="o">,[</span><span class="err">127</span>,<span class="err">128</span>,<span class="err">129</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">158</span>,<span class="err">159</span>,<span class="err">160</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">124</span>,<span class="err">125</span>,<span class="err">126</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">152</span>,<span class="err">153</span>,<span class="err">154</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">151</span>,<span class="err">152</span>,<span class="err">153</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">0</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">129</span>,<span class="err">130</span>,<span class="err">131</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">158</span>,<span class="err">159</span>,<span class="err">160</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">1</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">99</span>,<span class="err">100</span>,<span class="err">101</span>,<span class="kt">...|</span>
<span class="kt">|</span> <span class="err">0</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">154</span>,<span class="err">155</span>,<span class="err">156</span><span class="kt">...|</span>
<span class="kt">|</span> <span class="err">0</span><span class="kt">.</span><span class="err">0</span><span class="kt">|</span><span class="o">(</span><span class="err">780</span>,<span class="o">[</span><span class="err">127</span>,<span class="err">128</span>,<span class="err">129</span><span class="kt">...|</span>
<span class="kt">+-----+--------------------+</span>
<span class="kt">only</span> <span class="kt">showing</span> <span class="kt">top</span> <span class="err">10</span> <span class="kt">rows</span></code></pre></figure>
</div>
<div data-lang="java">
<p><a href="api/java/org/apache/spark/ml/source/libsvm/LibSVMDataSource.html"><code class="language-plaintext highlighter-rouge">LibSVMDataSource</code></a>
implements Spark SQL data source API for loading <code class="language-plaintext highlighter-rouge">LIBSVM</code> data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-java" data-lang="java"><span class="nc">Dataset</span><span class="o">&lt;</span><span class="nc">Row</span><span class="o">&gt;</span> <span class="n">df</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="na">read</span><span class="o">.</span><span class="na">format</span><span class="o">(</span><span class="s">"libsvm"</span><span class="o">).</span><span class="na">option</span><span class="o">(</span><span class="s">"numFeatures"</span><span class="o">,</span> <span class="s">"780"</span><span class="o">).</span><span class="na">load</span><span class="o">(</span><span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span>
<span class="n">df</span><span class="o">.</span><span class="na">show</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span>
<span class="cm">/*
Will output:
+-----+--------------------+
|label| features|
+-----+--------------------+
| 0.0|(780,[127,128,129...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[124,125,126...|
| 1.0|(780,[152,153,154...|
| 1.0|(780,[151,152,153...|
| 0.0|(780,[129,130,131...|
| 1.0|(780,[158,159,160...|
| 1.0|(780,[99,100,101,...|
| 0.0|(780,[154,155,156...|
| 0.0|(780,[127,128,129...|
+-----+--------------------+
only showing top 10 rows
*/</span></code></pre></figure>
</div>
<div data-lang="r">
<p>In SparkR we provide Spark SQL data source API for loading <code class="language-plaintext highlighter-rouge">LIBSVM</code> data as a DataFrame.</p>
<figure class="highlight"><pre><code class="language-r" data-lang="r"><span class="o">&gt;</span><span class="w"> </span><span class="n">df</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">read.df</span><span class="p">(</span><span class="s2">"data/mllib/sample_libsvm_data.txt"</span><span class="p">,</span><span class="w"> </span><span class="s2">"libsvm"</span><span class="p">)</span><span class="w">
</span><span class="o">&gt;</span><span class="w"> </span><span class="n">head</span><span class="p">(</span><span class="n">select</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">label</span><span class="p">,</span><span class="w"> </span><span class="n">df</span><span class="o">$</span><span class="n">features</span><span class="p">),</span><span class="w"> </span><span class="m">10</span><span class="p">)</span><span class="w">
</span><span class="n">label</span><span class="w"> </span><span class="n">features</span><span class="w">
</span><span class="m">1</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d35366e8</span><span class="o">&gt;</span><span class="w">
</span><span class="m">2</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d353bf78</span><span class="o">&gt;</span><span class="w">
</span><span class="m">3</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d3541840</span><span class="o">&gt;</span><span class="w">
</span><span class="m">4</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d3545108</span><span class="o">&gt;</span><span class="w">
</span><span class="m">5</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d354c8e0</span><span class="o">&gt;</span><span class="w">
</span><span class="m">6</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d35501a8</span><span class="o">&gt;</span><span class="w">
</span><span class="m">7</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d3555a70</span><span class="o">&gt;</span><span class="w">
</span><span class="m">8</span><span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d3559338</span><span class="o">&gt;</span><span class="w">
</span><span class="m">9</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d355cc00</span><span class="o">&gt;</span><span class="w">
</span><span class="m">10</span><span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="o">&lt;</span><span class="n">environment</span><span class="o">:</span><span class="w"> </span><span class="mh">0x7fe6d35643d8</span><span class="o">&gt;</span></code></pre></figure>
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