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<div class="section" id="quickstart">
<h1>QuickStart<a class="headerlink" href="#quickstart" title="Permalink to this headline">¶</a></h1>
<p>Let’s take a look at some code examples.</p>
<div class="section" id="matrix-operations">
<h2>Matrix Operations<a class="headerlink" href="#matrix-operations" title="Permalink to this headline">¶</a></h2>
<p>Making use of SystemDS, let us multiply an Matrix with an scalar:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import SystemDSContext</span>
<span class="kn">from</span> <span class="nn">systemds.context</span> <span class="kn">import</span> <span class="n">SystemDSContext</span>
<span class="c1"># Create a context and if necessary (no SystemDS py4j instance running)</span>
<span class="c1"># it starts a subprocess which does the execution in SystemDS</span>
<span class="k">with</span> <span class="n">SystemDSContext</span><span class="p">()</span> <span class="k">as</span> <span class="n">sds</span><span class="p">:</span>
<span class="c1"># Full generates a matrix completely filled with one number.</span>
<span class="c1"># Generate a 5x10 matrix filled with 4.2</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">sds</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mf">4.20</span><span class="p">)</span>
<span class="c1"># multiply with scalar. Nothing is executed yet!</span>
<span class="n">m_res</span> <span class="o">=</span> <span class="n">m</span> <span class="o">*</span> <span class="mf">3.1</span>
<span class="c1"># Do the calculation in SystemDS by calling compute().</span>
<span class="c1"># The returned value is an numpy array that can be directly printed.</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_res</span><span class="o">.</span><span class="n">compute</span><span class="p">())</span>
<span class="c1"># context will automatically be closed and process stopped</span>
</pre></div>
</div>
<p>As output we get:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span> <span class="mf">13.02</span><span class="p">]]</span>
</pre></div>
</div>
<p>The Python SystemDS package is compatible with numpy arrays.
Let us do a quick element-wise matrix multiplication of numpy arrays with SystemDS.
Remember to first start up a new terminal:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="c1"># import numpy</span>
<span class="c1"># Import SystemDSContext</span>
<span class="kn">from</span> <span class="nn">systemds.context</span> <span class="kn">import</span> <span class="n">SystemDSContext</span>
<span class="kn">from</span> <span class="nn">systemds.matrix</span> <span class="kn">import</span> <span class="n">Matrix</span>
<span class="c1"># create a random array</span>
<span class="n">m1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span> <span class="o">*</span> <span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.01</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
<span class="n">m1</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="c1"># create another random array</span>
<span class="n">m2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span> <span class="o">*</span> <span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
<span class="n">m2</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="c1"># Create a context</span>
<span class="k">with</span> <span class="n">SystemDSContext</span><span class="p">()</span> <span class="k">as</span> <span class="n">sds</span><span class="p">:</span>
<span class="c1"># element-wise matrix multiplication, note that nothing is executed yet!</span>
<span class="n">m_res</span> <span class="o">=</span> <span class="n">Matrix</span><span class="p">(</span><span class="n">sds</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span> <span class="o">*</span> <span class="n">Matrix</span><span class="p">(</span><span class="n">sds</span><span class="p">,</span> <span class="n">m2</span><span class="p">)</span>
<span class="c1"># lets do the actual computation in SystemDS! The result is an numpy array</span>
<span class="n">m_res_np</span> <span class="o">=</span> <span class="n">m_res</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">m_res_np</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="more-complex-operations">
<h2>More complex operations<a class="headerlink" href="#more-complex-operations" title="Permalink to this headline">¶</a></h2>
<p>SystemDS provides algorithm level functions as built-in functions to simplify development.
One example of this is l2SVM, a high level functions for Data-Scientists. Let’s take a look at l2svm:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Import numpy and SystemDS matrix</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">systemds.context</span> <span class="kn">import</span> <span class="n">SystemDSContext</span>
<span class="kn">from</span> <span class="nn">systemds.matrix</span> <span class="kn">import</span> <span class="n">Matrix</span>
<span class="kn">from</span> <span class="nn">systemds.operator.algorithm</span> <span class="kn">import</span> <span class="n">l2svm</span>
<span class="c1"># Set a seed</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Generate random features and labels in numpy</span>
<span class="c1"># This can easily be exchanged with a data set.</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.01</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
<span class="n">features</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="c1"># l2svm labels can only be 0 or 1</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">:</span>
<span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># compute our model</span>
<span class="k">with</span> <span class="n">SystemDSContext</span><span class="p">()</span> <span class="k">as</span> <span class="n">sds</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">l2svm</span><span class="p">(</span><span class="n">Matrix</span><span class="p">(</span><span class="n">sds</span><span class="p">,</span> <span class="n">features</span><span class="p">),</span> <span class="n">Matrix</span><span class="p">(</span><span class="n">sds</span><span class="p">,</span> <span class="n">labels</span><span class="p">))</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div>
</div>
<p>The output should be similar to:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[[</span> <span class="mf">0.02033445</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.00324092</span><span class="p">]</span>
<span class="p">[</span> <span class="mf">0.0014692</span> <span class="p">]</span>
<span class="p">[</span> <span class="mf">0.02649209</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.00616902</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.0095087</span> <span class="p">]</span>
<span class="p">[</span> <span class="mf">0.01039221</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.0011352</span> <span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.01686351</span><span class="p">]</span>
<span class="p">[</span><span class="o">-</span><span class="mf">0.03839821</span><span class="p">]]</span>
</pre></div>
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