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<title>Streaming Data — daal4py 2021.1 documentation</title>
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daal4py
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2021
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<li class="toctree-l1"><a class="reference internal" href="index.html">About daal4py</a></li>
<li class="toctree-l1"><a class="reference internal" href="data.html">Data</a></li>
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<li class="toctree-l1"><a class="reference internal" href="algorithms.html">Supported Algorithms</a></li>
<li class="toctree-l1"><a class="reference internal" href="scaling.html">Distributed Mode</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Streaming Mode</a><ul>
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<section id="streaming-data">
<span id="streaming"></span><h1>Streaming Data<a class="headerlink" href="#streaming-data" title="Permalink to this heading"></a></h1>
<div class="admonition note" id="note">
<p class="admonition-title">Note</p>
<p>Scikit-learn patching functionality in daal4py was deprecated and moved to a separate package, <a class="reference external" href="https://github.com/intel/scikit-learn-intelex">Intel(R) Extension for Scikit-learn*</a>.
All future patches will be available only in Intel(R) Extension for Scikit-learn*. Use the scikit-learn-intelex package instead of daal4py for the scikit-learn acceleration.</p>
</div>
<p>For large quantities of data it might be impossible to provide all input data at
once. This might be because the data resides in multiple files and merging it is
to costly (or not feasible in other ways). In other cases the data is simply too
large to be loaded completely into memory. Or, the data might come in as an
actual stream. daal4py’s streaming mode allows you to process such data.</p>
<p>Besides supporting certain use cases, streaming also allows interleaving I/O
operations with computation.</p>
<p>daal4py’s streaming mode is as easy as follows:</p>
<ol class="arabic">
<li><p>When constructing the algorithm configure it with <code class="docutils literal notranslate"><span class="pre">streaming=True</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">algo</span> <span class="o">=</span> <span class="n">daal4py</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">streaming</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>Repeat calling <code class="docutils literal notranslate"><span class="pre">compute(input-data)</span></code> with chunks of your input (arrays, DataFrames or
files):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">input_files</span><span class="p">:</span>
<span class="n">algo</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>When done with inputting, call <code class="docutils literal notranslate"><span class="pre">finalize()</span></code> to obtain the result:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">algo</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span>
</pre></div>
</div>
</li>
</ol>
<p>The streaming algorithms also accept arrays and DataFrames as input, e.g. the
data can come from a stream rather than from multiple files. Here is an example
which simulates a data stream using a generator which reads a file in chunks:
<a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/stream.py">SVD reading stream of data</a></p>
<section id="supported-algorithms-and-examples">
<h2>Supported Algorithms and Examples<a class="headerlink" href="#supported-algorithms-and-examples" title="Permalink to this heading"></a></h2>
<p>The following algorithms support streaming:</p>
<ul class="simple">
<li><p>SVD (svd)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/svd_streaming.py">SVD</a></p></li>
</ul>
</li>
<li><p>Linear Regression Training (linear_regression_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/linear_regression_streaming.py">Linear Regression</a></p></li>
</ul>
</li>
<li><p>Ridge Regression Training (ridge_regression_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/ridge_regression_streaming.py">Ridge Regression</a></p></li>
</ul>
</li>
<li><p>Multinomial Naive Bayes Training (multinomial_naive_bayes_training)</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/naive_bayes_streaming.py">Naive Bayes</a></p></li>
</ul>
</li>
<li><p>Moments of Low Order</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/low_order_moms_streaming.py">Low Order Moments</a></p></li>
</ul>
</li>
<li><p>Covariance</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/covariance_streaming.py">Covariance</a></p></li>
</ul>
</li>
<li><p>QR</p>
<ul>
<li><p><a class="reference external" href="https://github.com/intel/scikit-learn-intelex/tree/main/examples/daal4py/qr_streaming.py">QR</a></p></li>
</ul>
</li>
</ul>
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