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| <div class="section" id="isotonicregressionmodel"> |
| <h1>IsotonicRegressionModel<a class="headerlink" href="#isotonicregressionmodel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.regression.IsotonicRegressionModel"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.regression.</code><code class="sig-name descname">IsotonicRegressionModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">boundaries</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">predictions</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">isotonic</span><span class="p">:</span> <span class="n">bool</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#IsotonicRegressionModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.IsotonicRegressionModel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Regression model for isotonic regression.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| <dl class="field-list simple"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl class="simple"> |
| <dt><strong>boundaries</strong><span class="classifier">ndarray</span></dt><dd><p>Array of boundaries for which predictions are known. Boundaries |
| must be sorted in increasing order.</p> |
| </dd> |
| <dt><strong>predictions</strong><span class="classifier">ndarray</span></dt><dd><p>Array of predictions associated to the boundaries at the same |
| index. Results of isotonic regression and therefore monotone.</p> |
| </dd> |
| <dt><strong>isotonic</strong><span class="classifier">true</span></dt><dd><p>Indicates whether this is isotonic or antitonic.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">17</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span> |
| <span class="gp">>>> </span><span class="n">irm</span> <span class="o">=</span> <span class="n">IsotonicRegression</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">))</span> |
| <span class="gp">>>> </span><span class="n">irm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> |
| <span class="go">2.0</span> |
| <span class="gp">>>> </span><span class="n">irm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="go">16.5</span> |
| <span class="gp">>>> </span><span class="n">irm</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]))</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span> |
| <span class="go">[2.0, 16.5]</span> |
| <span class="gp">>>> </span><span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">tempfile</span> |
| <span class="gp">>>> </span><span class="n">path</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span> |
| <span class="gp">>>> </span><span class="n">irm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span> <span class="o">=</span> <span class="n">IsotonicRegressionModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> |
| <span class="go">2.0</span> |
| <span class="gp">>>> </span><span class="n">sameModel</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="go">16.5</span> |
| <span class="gp">>>> </span><span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span> |
| <span class="gp">>>> </span><span class="k">try</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="n">rmtree</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> |
| <span class="gp">... </span><span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span> |
| <span class="gp">... </span> <span class="k">pass</span> |
| </pre></div> |
| </div> |
| <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.regression.IsotonicRegressionModel.load" title="pyspark.mllib.regression.IsotonicRegressionModel.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(sc, path)</p></td> |
| <td><p>Load an IsotonicRegressionModel.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.regression.IsotonicRegressionModel.predict" title="pyspark.mllib.regression.IsotonicRegressionModel.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(x)</p></td> |
| <td><p>Predict labels for provided features.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.regression.IsotonicRegressionModel.save" title="pyspark.mllib.regression.IsotonicRegressionModel.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(sc, path)</p></td> |
| <td><p>Save an IsotonicRegressionModel.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.regression.IsotonicRegressionModel.load"> |
| <em class="property">classmethod </em><code class="sig-name descname">load</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="#pyspark.mllib.regression.IsotonicRegressionModel" title="pyspark.mllib.regression.IsotonicRegressionModel">pyspark.mllib.regression.IsotonicRegressionModel</a><a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#IsotonicRegressionModel.load"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.IsotonicRegressionModel.load" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Load an IsotonicRegressionModel.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.regression.IsotonicRegressionModel.predict"> |
| <code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">x</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>float<span class="p">, </span>VectorLike<span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>float<span class="p">]</span><span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>VectorLike<span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span> → Union<span class="p">[</span>numpy.float64<span class="p">, </span>numpy.ndarray<span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>numpy.float64<span class="p">]</span><span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>numpy.ndarray<span class="p">]</span><span class="p">]</span><a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#IsotonicRegressionModel.predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.IsotonicRegressionModel.predict" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Predict labels for provided features. |
| Using a piecewise linear function. |
| 1) If x exactly matches a boundary then associated prediction |
| is returned. In case there are multiple predictions with the |
| same boundary then one of them is returned. Which one is |
| undefined (same as java.util.Arrays.binarySearch). |
| 2) If x is lower or higher than all boundaries then first or |
| last prediction is returned respectively. In case there are |
| multiple predictions with the same boundary then the lowest |
| or highest is returned respectively. |
| 3) If x falls between two values in boundary array then |
| prediction is treated as piecewise linear function and |
| interpolated value is returned. In case there are multiple |
| values with the same boundary then the same rules as in 2) |
| are used.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>x</strong><span class="classifier"><a class="reference internal" href="pyspark.mllib.linalg.Vector.html#pyspark.mllib.linalg.Vector" title="pyspark.mllib.linalg.Vector"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.mllib.linalg.Vector</span></code></a> or <a class="reference internal" href="pyspark.RDD.html#pyspark.RDD" title="pyspark.RDD"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD</span></code></a></span></dt><dd><p>Feature or RDD of Features to be labeled.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.regression.IsotonicRegressionModel.save"> |
| <code class="sig-name descname">save</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sc</span><span class="p">:</span> <span class="n">pyspark.context.SparkContext</span></em>, <em class="sig-param"><span class="n">path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="reference internal" href="../../_modules/pyspark/mllib/regression.html#IsotonicRegressionModel.save"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.regression.IsotonicRegressionModel.save" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Save an IsotonicRegressionModel.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
| |
| </div> |
| |
| |
| </div> |
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