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| <div class="section" id="streaminglogisticregressionwithsgd"> |
| <h1>StreamingLogisticRegressionWithSGD<a class="headerlink" href="#streaminglogisticregressionwithsgd" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.classification.</code><code class="sig-name descname">StreamingLogisticRegressionWithSGD</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">stepSize</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">numIterations</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">50</span></em>, <em class="sig-param"><span class="n">miniBatchFraction</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1.0</span></em>, <em class="sig-param"><span class="n">regParam</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.0</span></em>, <em class="sig-param"><span class="n">convergenceTol</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.001</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#StreamingLogisticRegressionWithSGD"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train or predict a logistic regression model on streaming data. |
| Training uses Stochastic Gradient Descent to update the model based on |
| each new batch of incoming data from a DStream.</p> |
| <p>Each batch of data is assumed to be an RDD of LabeledPoints. |
| The number of data points per batch can vary, but the number |
| of features must be constant. An initial weight |
| vector must be provided.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>stepSize</strong><span class="classifier">float, optional</span></dt><dd><p>Step size for each iteration of gradient descent. |
| (default: 0.1)</p> |
| </dd> |
| <dt><strong>numIterations</strong><span class="classifier">int, optional</span></dt><dd><p>Number of iterations run for each batch of data. |
| (default: 50)</p> |
| </dd> |
| <dt><strong>miniBatchFraction</strong><span class="classifier">float, optional</span></dt><dd><p>Fraction of each batch of data to use for updates. |
| (default: 1.0)</p> |
| </dd> |
| <dt><strong>regParam</strong><span class="classifier">float, optional</span></dt><dd><p>L2 Regularization parameter. |
| (default: 0.0)</p> |
| </dd> |
| <dt><strong>convergenceTol</strong><span class="classifier">float, optional</span></dt><dd><p>Value used to determine when to terminate iterations. |
| (default: 0.001)</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.latestModel" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.latestModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">latestModel</span></code></a>()</p></td> |
| <td><p>Returns the latest model.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOn" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictOn</span></code></a>(dstream)</p></td> |
| <td><p>Use the model to make predictions on batches of data from a DStream.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOnValues" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOnValues"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predictOnValues</span></code></a>(dstream)</p></td> |
| <td><p>Use the model to make predictions on the values of a DStream and carry over its keys.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.setInitialWeights" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.setInitialWeights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setInitialWeights</span></code></a>(initialWeights)</p></td> |
| <td><p>Set the initial value of weights.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.trainOn" title="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.trainOn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trainOn</span></code></a>(dstream)</p></td> |
| <td><p>Train the model on the incoming dstream.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.latestModel"> |
| <code class="sig-name descname">latestModel</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Optional<span class="p">[</span><a class="reference internal" href="pyspark.mllib.regression.LinearModel.html#pyspark.mllib.regression.LinearModel" title="pyspark.mllib.regression.LinearModel">pyspark.mllib.regression.LinearModel</a><span class="p">]</span><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.latestModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the latest model.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOn"> |
| <code class="sig-name descname">predictOn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dstream</span><span class="p">:</span> <span class="n">DStream<span class="p">[</span>VectorLike<span class="p">]</span></span></em><span class="sig-paren">)</span> → DStream<span class="p">[</span>float<span class="p">]</span><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Use the model to make predictions on batches of data from a |
| DStream.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyspark.streaming.DStream.html#pyspark.streaming.DStream" title="pyspark.streaming.DStream"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.streaming.DStream</span></code></a></dt><dd><p>DStream containing predictions.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOnValues"> |
| <code class="sig-name descname">predictOnValues</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dstream</span><span class="p">:</span> <span class="n">DStream<span class="p">[</span>Tuple<span class="p">[</span>K<span class="p">, </span>VectorLike<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → DStream<span class="p">[</span>Tuple<span class="p">[</span>K<span class="p">, </span>float<span class="p">]</span><span class="p">]</span><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.predictOnValues" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Use the model to make predictions on the values of a DStream and |
| carry over its keys.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Returns</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><a class="reference internal" href="pyspark.streaming.DStream.html#pyspark.streaming.DStream" title="pyspark.streaming.DStream"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.streaming.DStream</span></code></a></dt><dd><p>DStream containing predictions.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.setInitialWeights"> |
| <code class="sig-name descname">setInitialWeights</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">initialWeights</span><span class="p">:</span> <span class="n">VectorLike</span></em><span class="sig-paren">)</span> → StreamingLogisticRegressionWithSGD<a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#StreamingLogisticRegressionWithSGD.setInitialWeights"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.setInitialWeights" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Set the initial value of weights.</p> |
| <p>This must be set before running trainOn and predictOn.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.trainOn"> |
| <code class="sig-name descname">trainOn</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dstream</span><span class="p">:</span> <span class="n">pyspark.streaming.dstream.DStream<span class="p">[</span><a class="reference internal" href="pyspark.mllib.regression.LabeledPoint.html#pyspark.mllib.regression.LabeledPoint" title="pyspark.mllib.regression.LabeledPoint">pyspark.mllib.regression.LabeledPoint</a><span class="p">]</span></span></em><span class="sig-paren">)</span> → None<a class="reference internal" href="../../_modules/pyspark/mllib/classification.html#StreamingLogisticRegressionWithSGD.trainOn"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.classification.StreamingLogisticRegressionWithSGD.trainOn" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Train the model on the incoming dstream.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.5.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
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