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<h1>Source code for pyspark.mllib.tree</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span>
<span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c1"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c1"># this work for additional information regarding copyright ownership.</span>
<span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c1"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c1"># the License. You may obtain a copy of the License at</span>
<span class="c1">#</span>
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c1">#</span>
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c1"># See the License for the specific language governing permissions and</span>
<span class="c1"># limitations under the License.</span>
<span class="c1">#</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">RDD</span><span class="p">,</span> <span class="n">since</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">callMLlibFunc</span><span class="p">,</span> <span class="n">inherit_doc</span><span class="p">,</span> <span class="n">JavaModelWrapper</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">_convert_to_vector</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">JavaLoader</span><span class="p">,</span> <span class="n">JavaSaveable</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">overload</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
<span class="kn">from</span> <span class="nn">pyspark.rdd</span> <span class="kn">import</span> <span class="n">RDD</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib._typing</span> <span class="kn">import</span> <span class="n">VectorLike</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">&quot;DecisionTreeModel&quot;</span><span class="p">,</span>
<span class="s2">&quot;DecisionTree&quot;</span><span class="p">,</span>
<span class="s2">&quot;RandomForestModel&quot;</span><span class="p">,</span>
<span class="s2">&quot;RandomForest&quot;</span><span class="p">,</span>
<span class="s2">&quot;GradientBoostedTreesModel&quot;</span><span class="p">,</span>
<span class="s2">&quot;GradientBoostedTrees&quot;</span><span class="p">,</span>
<span class="p">]</span>
<span class="k">class</span> <span class="nc">TreeEnsembleModel</span><span class="p">(</span><span class="n">JavaModelWrapper</span><span class="p">,</span> <span class="n">JavaSaveable</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;TreeEnsembleModel</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">&quot;VectorLike&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
<span class="o">...</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">,</span> <span class="n">RDD</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">float</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predict values for a single data point or an RDD of points using</span>
<span class="sd"> the model trained.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> In Python, predict cannot currently be used within an RDD</span>
<span class="sd"> transformation or action.</span>
<span class="sd"> Call predict directly on the RDD instead.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">RDD</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">_convert_to_vector</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="n">_convert_to_vector</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.3.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">numTrees</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get number of trees in ensemble.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;numTrees&quot;</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.3.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">totalNumNodes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get total number of nodes, summed over all trees in the ensemble.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;totalNumNodes&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Summary of model&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">toString</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.3.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">toDebugString</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Full model&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">toDebugString</span><span class="p">()</span>
<div class="viewcode-block" id="DecisionTreeModel"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel">[docs]</a><span class="k">class</span> <span class="nc">DecisionTreeModel</span><span class="p">(</span><span class="n">JavaModelWrapper</span><span class="p">,</span> <span class="n">JavaSaveable</span><span class="p">,</span> <span class="n">JavaLoader</span><span class="p">[</span><span class="s2">&quot;DecisionTreeModel&quot;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A decision tree model for classification or regression.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="s2">&quot;VectorLike&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
<span class="o">...</span>
<div class="viewcode-block" id="DecisionTreeModel.predict"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">,</span> <span class="n">RDD</span><span class="p">[</span><span class="s2">&quot;VectorLike&quot;</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">RDD</span><span class="p">[</span><span class="nb">float</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predict the label of one or more examples.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD`</span>
<span class="sd"> Data point (feature vector), or an RDD of data points (feature</span>
<span class="sd"> vectors).</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> In Python, predict cannot currently be used within an RDD</span>
<span class="sd"> transformation or action.</span>
<span class="sd"> Call predict directly on the RDD instead.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">RDD</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">_convert_to_vector</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">&quot;predict&quot;</span><span class="p">,</span> <span class="n">_convert_to_vector</span><span class="p">(</span><span class="n">x</span><span class="p">))</span></div>
<div class="viewcode-block" id="DecisionTreeModel.numNodes"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel.numNodes">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.1.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">numNodes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Get number of nodes in tree, including leaf nodes.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">numNodes</span><span class="p">()</span></div>
<div class="viewcode-block" id="DecisionTreeModel.depth"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel.depth">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.1.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">depth</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">depth</span><span class="p">()</span></div>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;summary of model.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">toString</span><span class="p">()</span>
<div class="viewcode-block" id="DecisionTreeModel.toDebugString"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTreeModel.html#pyspark.mllib.tree.DecisionTreeModel.toDebugString">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.2.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">toDebugString</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;full model.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_model</span><span class="o">.</span><span class="n">toDebugString</span><span class="p">()</span></div>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;org.apache.spark.mllib.tree.model.DecisionTreeModel&quot;</span></div>
<div class="viewcode-block" id="DecisionTree"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTree.html#pyspark.mllib.tree.DecisionTree">[docs]</a><span class="k">class</span> <span class="nc">DecisionTree</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Learning algorithm for a decision tree model for classification or</span>
<span class="sd"> regression.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_train</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="nb">type</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">features</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gini&quot;</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DecisionTreeModel</span><span class="p">:</span>
<span class="n">first</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first</span><span class="p">,</span> <span class="n">LabeledPoint</span><span class="p">),</span> <span class="s2">&quot;the data should be RDD of LabeledPoint&quot;</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">callMLlibFunc</span><span class="p">(</span>
<span class="s2">&quot;trainDecisionTreeModel&quot;</span><span class="p">,</span>
<span class="n">data</span><span class="p">,</span>
<span class="nb">type</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">,</span>
<span class="n">features</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">DecisionTreeModel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<div class="viewcode-block" id="DecisionTree.trainClassifier"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTree.html#pyspark.mllib.tree.DecisionTree.trainClassifier">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">trainClassifier</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">numClasses</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gini&quot;</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DecisionTreeModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a decision tree model for classification.</span>
<span class="sd"> .. versionadded:: 1.1.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : :py:class:`pyspark.RDD`</span>
<span class="sd"> Training data: RDD of LabeledPoint. Labels should take values</span>
<span class="sd"> {0, 1, ..., numClasses-1}.</span>
<span class="sd"> numClasses : int</span>
<span class="sd"> Number of classes for classification.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> impurity : str, optional</span>
<span class="sd"> Criterion used for information gain calculation.</span>
<span class="sd"> Supported values: &quot;gini&quot; or &quot;entropy&quot;.</span>
<span class="sd"> (default: &quot;gini&quot;)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 5)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Number of bins used for finding splits at each node.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> minInstancesPerNode : int, optional</span>
<span class="sd"> Minimum number of instances required at child nodes to create</span>
<span class="sd"> the parent split.</span>
<span class="sd"> (default: 1)</span>
<span class="sd"> minInfoGain : float, optional</span>
<span class="sd"> Minimum info gain required to create a split.</span>
<span class="sd"> (default: 0.0)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`DecisionTreeModel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from numpy import array</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import DecisionTree</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; data = [</span>
<span class="sd"> ... LabeledPoint(0.0, [0.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [1.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [2.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [3.0])</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {})</span>
<span class="sd"> &gt;&gt;&gt; print(model)</span>
<span class="sd"> DecisionTreeModel classifier of depth 1 with 3 nodes</span>
<span class="sd"> &gt;&gt;&gt; print(model.toDebugString())</span>
<span class="sd"> DecisionTreeModel classifier of depth 1 with 3 nodes</span>
<span class="sd"> If (feature 0 &lt;= 0.5)</span>
<span class="sd"> Predict: 0.0</span>
<span class="sd"> Else (feature 0 &gt; 0.5)</span>
<span class="sd"> Predict: 1.0</span>
<span class="sd"> &lt;BLANKLINE&gt;</span>
<span class="sd"> &gt;&gt;&gt; model.predict(array([1.0]))</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict(array([0.0]))</span>
<span class="sd"> 0.0</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[1.0], [0.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;classification&quot;</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">,</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="DecisionTree.trainRegressor"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.DecisionTree.html#pyspark.mllib.tree.DecisionTree.trainRegressor">[docs]</a> <span class="nd">@classmethod</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.1.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">trainRegressor</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DecisionTreeModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a decision tree model for regression.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : :py:class:`pyspark.RDD`</span>
<span class="sd"> Training data: RDD of LabeledPoint. Labels are real numbers.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> impurity : str, optional</span>
<span class="sd"> Criterion used for information gain calculation.</span>
<span class="sd"> The only supported value for regression is &quot;variance&quot;.</span>
<span class="sd"> (default: &quot;variance&quot;)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 5)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Number of bins used for finding splits at each node.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> minInstancesPerNode : int, optional</span>
<span class="sd"> Minimum number of instances required at child nodes to create</span>
<span class="sd"> the parent split.</span>
<span class="sd"> (default: 1)</span>
<span class="sd"> minInfoGain : float, optional</span>
<span class="sd"> Minimum info gain required to create a split.</span>
<span class="sd"> (default: 0.0)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`DecisionTreeModel`</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import DecisionTree</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.linalg import SparseVector</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; sparse_data = [</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {})</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {1: 1.0}))</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {1: 0.0}))</span>
<span class="sd"> 0.0</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[0.0, 1.0], [0.0, 0.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;regression&quot;</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">minInstancesPerNode</span><span class="p">,</span>
<span class="n">minInfoGain</span><span class="p">,</span>
<span class="p">)</span></div></div>
<div class="viewcode-block" id="RandomForestModel"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.RandomForestModel.html#pyspark.mllib.tree.RandomForestModel">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">RandomForestModel</span><span class="p">(</span><span class="n">TreeEnsembleModel</span><span class="p">,</span> <span class="n">JavaLoader</span><span class="p">[</span><span class="s2">&quot;RandomForestModel&quot;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Represents a random forest model.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;org.apache.spark.mllib.tree.model.RandomForestModel&quot;</span></div>
<div class="viewcode-block" id="RandomForest"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.RandomForest.html#pyspark.mllib.tree.RandomForest">[docs]</a><span class="k">class</span> <span class="nc">RandomForest</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Learning algorithm for a random forest model for classification or</span>
<span class="sd"> regression.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">supportedFeatureSubsetStrategies</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="s2">&quot;sqrt&quot;</span><span class="p">,</span> <span class="s2">&quot;log2&quot;</span><span class="p">,</span> <span class="s2">&quot;onethird&quot;</span><span class="p">)</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_train</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">algo</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">numTrees</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RandomForestModel</span><span class="p">:</span>
<span class="n">first</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first</span><span class="p">,</span> <span class="n">LabeledPoint</span><span class="p">),</span> <span class="s2">&quot;the data should be RDD of LabeledPoint&quot;</span>
<span class="k">if</span> <span class="n">featureSubsetStrategy</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">supportedFeatureSubsetStrategies</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;unsupported featureSubsetStrategy: </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">featureSubsetStrategy</span><span class="p">)</span>
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">seed</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">0</span><span class="p">,</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">callMLlibFunc</span><span class="p">(</span>
<span class="s2">&quot;trainRandomForestModel&quot;</span><span class="p">,</span>
<span class="n">data</span><span class="p">,</span>
<span class="n">algo</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">numTrees</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">seed</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">RandomForestModel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<div class="viewcode-block" id="RandomForest.trainClassifier"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.RandomForest.html#pyspark.mllib.tree.RandomForest.trainClassifier">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">trainClassifier</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">numClasses</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">numTrees</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;auto&quot;</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gini&quot;</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RandomForestModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a random forest model for binary or multiclass</span>
<span class="sd"> classification.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : :py:class:`pyspark.RDD`</span>
<span class="sd"> Training dataset: RDD of LabeledPoint. Labels should take values</span>
<span class="sd"> {0, 1, ..., numClasses-1}.</span>
<span class="sd"> numClasses : int</span>
<span class="sd"> Number of classes for classification.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> numTrees : int</span>
<span class="sd"> Number of trees in the random forest.</span>
<span class="sd"> featureSubsetStrategy : str, optional</span>
<span class="sd"> Number of features to consider for splits at each node.</span>
<span class="sd"> Supported values: &quot;auto&quot;, &quot;all&quot;, &quot;sqrt&quot;, &quot;log2&quot;, &quot;onethird&quot;.</span>
<span class="sd"> If &quot;auto&quot; is set, this parameter is set based on numTrees:</span>
<span class="sd"> if numTrees == 1, set to &quot;all&quot;;</span>
<span class="sd"> if numTrees &gt; 1 (forest) set to &quot;sqrt&quot;.</span>
<span class="sd"> (default: &quot;auto&quot;)</span>
<span class="sd"> impurity : str, optional</span>
<span class="sd"> Criterion used for information gain calculation.</span>
<span class="sd"> Supported values: &quot;gini&quot; or &quot;entropy&quot;.</span>
<span class="sd"> (default: &quot;gini&quot;)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 4)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Maximum number of bins used for splitting features.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> seed : int, Optional</span>
<span class="sd"> Random seed for bootstrapping and choosing feature subsets.</span>
<span class="sd"> Set as None to generate seed based on system time.</span>
<span class="sd"> (default: None)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`RandomForestModel`</span>
<span class="sd"> that can be used for prediction.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import RandomForest</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; data = [</span>
<span class="sd"> ... LabeledPoint(0.0, [0.0]),</span>
<span class="sd"> ... LabeledPoint(0.0, [1.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [2.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [3.0])</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt; model = RandomForest.trainClassifier(sc.parallelize(data), 2, {}, 3, seed=42)</span>
<span class="sd"> &gt;&gt;&gt; model.numTrees()</span>
<span class="sd"> 3</span>
<span class="sd"> &gt;&gt;&gt; model.totalNumNodes()</span>
<span class="sd"> 7</span>
<span class="sd"> &gt;&gt;&gt; print(model)</span>
<span class="sd"> TreeEnsembleModel classifier with 3 trees</span>
<span class="sd"> &lt;BLANKLINE&gt;</span>
<span class="sd"> &gt;&gt;&gt; print(model.toDebugString())</span>
<span class="sd"> TreeEnsembleModel classifier with 3 trees</span>
<span class="sd"> &lt;BLANKLINE&gt;</span>
<span class="sd"> Tree 0:</span>
<span class="sd"> Predict: 1.0</span>
<span class="sd"> Tree 1:</span>
<span class="sd"> If (feature 0 &lt;= 1.5)</span>
<span class="sd"> Predict: 0.0</span>
<span class="sd"> Else (feature 0 &gt; 1.5)</span>
<span class="sd"> Predict: 1.0</span>
<span class="sd"> Tree 2:</span>
<span class="sd"> If (feature 0 &lt;= 1.5)</span>
<span class="sd"> Predict: 0.0</span>
<span class="sd"> Else (feature 0 &gt; 1.5)</span>
<span class="sd"> Predict: 1.0</span>
<span class="sd"> &lt;BLANKLINE&gt;</span>
<span class="sd"> &gt;&gt;&gt; model.predict([2.0])</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict([0.0])</span>
<span class="sd"> 0.0</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[3.0], [1.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;classification&quot;</span><span class="p">,</span>
<span class="n">numClasses</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">numTrees</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">seed</span><span class="p">,</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="RandomForest.trainRegressor"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.RandomForest.html#pyspark.mllib.tree.RandomForest.trainRegressor">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">trainRegressor</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">numTrees</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;auto&quot;</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;variance&quot;</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">RandomForestModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a random forest model for regression.</span>
<span class="sd"> .. versionadded:: 1.2.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : :py:class:`pyspark.RDD`</span>
<span class="sd"> Training dataset: RDD of LabeledPoint. Labels are real numbers.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> numTrees : int</span>
<span class="sd"> Number of trees in the random forest.</span>
<span class="sd"> featureSubsetStrategy : str, optional</span>
<span class="sd"> Number of features to consider for splits at each node.</span>
<span class="sd"> Supported values: &quot;auto&quot;, &quot;all&quot;, &quot;sqrt&quot;, &quot;log2&quot;, &quot;onethird&quot;.</span>
<span class="sd"> If &quot;auto&quot; is set, this parameter is set based on numTrees:</span>
<span class="sd"> - if numTrees == 1, set to &quot;all&quot;;</span>
<span class="sd"> - if numTrees &gt; 1 (forest) set to &quot;onethird&quot; for regression.</span>
<span class="sd"> (default: &quot;auto&quot;)</span>
<span class="sd"> impurity : str, optional</span>
<span class="sd"> Criterion used for information gain calculation.</span>
<span class="sd"> The only supported value for regression is &quot;variance&quot;.</span>
<span class="sd"> (default: &quot;variance&quot;)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 4)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Maximum number of bins used for splitting features.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> seed : int, optional</span>
<span class="sd"> Random seed for bootstrapping and choosing feature subsets.</span>
<span class="sd"> Set as None to generate seed based on system time.</span>
<span class="sd"> (default: None)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`RandomForestModel`</span>
<span class="sd"> that can be used for prediction.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import RandomForest</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.linalg import SparseVector</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; sparse_data = [</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; model = RandomForest.trainRegressor(sc.parallelize(sparse_data), {}, 2, seed=42)</span>
<span class="sd"> &gt;&gt;&gt; model.numTrees()</span>
<span class="sd"> 2</span>
<span class="sd"> &gt;&gt;&gt; model.totalNumNodes()</span>
<span class="sd"> 4</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {1: 1.0}))</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {0: 1.0}))</span>
<span class="sd"> 0.5</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[0.0, 1.0], [1.0, 0.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.5]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;regression&quot;</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">numTrees</span><span class="p">,</span>
<span class="n">featureSubsetStrategy</span><span class="p">,</span>
<span class="n">impurity</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="n">seed</span><span class="p">,</span>
<span class="p">)</span></div></div>
<div class="viewcode-block" id="GradientBoostedTreesModel"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.GradientBoostedTreesModel.html#pyspark.mllib.tree.GradientBoostedTreesModel">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">GradientBoostedTreesModel</span><span class="p">(</span><span class="n">TreeEnsembleModel</span><span class="p">,</span> <span class="n">JavaLoader</span><span class="p">[</span><span class="s2">&quot;GradientBoostedTreesModel&quot;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Represents a gradient-boosted tree model.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">&quot;org.apache.spark.mllib.tree.model.GradientBoostedTreesModel&quot;</span></div>
<div class="viewcode-block" id="GradientBoostedTrees"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.GradientBoostedTrees.html#pyspark.mllib.tree.GradientBoostedTrees">[docs]</a><span class="k">class</span> <span class="nc">GradientBoostedTrees</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Learning algorithm for a gradient boosted trees model for</span>
<span class="sd"> classification or regression.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_train</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">algo</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">loss</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">GradientBoostedTreesModel</span><span class="p">:</span>
<span class="n">first</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first</span><span class="p">,</span> <span class="n">LabeledPoint</span><span class="p">),</span> <span class="s2">&quot;the data should be RDD of LabeledPoint&quot;</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">callMLlibFunc</span><span class="p">(</span>
<span class="s2">&quot;trainGradientBoostedTreesModel&quot;</span><span class="p">,</span>
<span class="n">data</span><span class="p">,</span>
<span class="n">algo</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">loss</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">GradientBoostedTreesModel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<div class="viewcode-block" id="GradientBoostedTrees.trainClassifier"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.GradientBoostedTrees.html#pyspark.mllib.tree.GradientBoostedTrees.trainClassifier">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">trainClassifier</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">loss</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;logLoss&quot;</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">GradientBoostedTreesModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a gradient-boosted trees model for classification.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data : :py:class:`pyspark.RDD`</span>
<span class="sd"> Training dataset: RDD of LabeledPoint. Labels should take values</span>
<span class="sd"> {0, 1}.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> loss : str, optional</span>
<span class="sd"> Loss function used for minimization during gradient boosting.</span>
<span class="sd"> Supported values: &quot;logLoss&quot;, &quot;leastSquaresError&quot;,</span>
<span class="sd"> &quot;leastAbsoluteError&quot;.</span>
<span class="sd"> (default: &quot;logLoss&quot;)</span>
<span class="sd"> numIterations : int, optional</span>
<span class="sd"> Number of iterations of boosting.</span>
<span class="sd"> (default: 100)</span>
<span class="sd"> learningRate : float, optional</span>
<span class="sd"> Learning rate for shrinking the contribution of each estimator.</span>
<span class="sd"> The learning rate should be between in the interval (0, 1].</span>
<span class="sd"> (default: 0.1)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 3)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Maximum number of bins used for splitting features. DecisionTree</span>
<span class="sd"> requires maxBins &gt;= max categories.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`GradientBoostedTreesModel`</span>
<span class="sd"> that can be used for prediction.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import GradientBoostedTrees</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; data = [</span>
<span class="sd"> ... LabeledPoint(0.0, [0.0]),</span>
<span class="sd"> ... LabeledPoint(0.0, [1.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [2.0]),</span>
<span class="sd"> ... LabeledPoint(1.0, [3.0])</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}, numIterations=10)</span>
<span class="sd"> &gt;&gt;&gt; model.numTrees()</span>
<span class="sd"> 10</span>
<span class="sd"> &gt;&gt;&gt; model.totalNumNodes()</span>
<span class="sd"> 30</span>
<span class="sd"> &gt;&gt;&gt; print(model) # it already has newline</span>
<span class="sd"> TreeEnsembleModel classifier with 10 trees</span>
<span class="sd"> &lt;BLANKLINE&gt;</span>
<span class="sd"> &gt;&gt;&gt; model.predict([2.0])</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict([0.0])</span>
<span class="sd"> 0.0</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[2.0], [0.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;classification&quot;</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">loss</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="p">)</span></div>
<div class="viewcode-block" id="GradientBoostedTrees.trainRegressor"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.tree.GradientBoostedTrees.html#pyspark.mllib.tree.GradientBoostedTrees.trainRegressor">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">trainRegressor</span><span class="p">(</span>
<span class="bp">cls</span><span class="p">,</span>
<span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">LabeledPoint</span><span class="p">],</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
<span class="n">loss</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;leastSquaresError&quot;</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">GradientBoostedTreesModel</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Train a gradient-boosted trees model for regression.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data :</span>
<span class="sd"> Training dataset: RDD of LabeledPoint. Labels are real numbers.</span>
<span class="sd"> categoricalFeaturesInfo : dict</span>
<span class="sd"> Map storing arity of categorical features. An entry (n -&gt; k)</span>
<span class="sd"> indicates that feature n is categorical with k categories</span>
<span class="sd"> indexed from 0: {0, 1, ..., k-1}.</span>
<span class="sd"> loss : str, optional</span>
<span class="sd"> Loss function used for minimization during gradient boosting.</span>
<span class="sd"> Supported values: &quot;logLoss&quot;, &quot;leastSquaresError&quot;,</span>
<span class="sd"> &quot;leastAbsoluteError&quot;.</span>
<span class="sd"> (default: &quot;leastSquaresError&quot;)</span>
<span class="sd"> numIterations : int, optional</span>
<span class="sd"> Number of iterations of boosting.</span>
<span class="sd"> (default: 100)</span>
<span class="sd"> learningRate : float, optional</span>
<span class="sd"> Learning rate for shrinking the contribution of each estimator.</span>
<span class="sd"> The learning rate should be between in the interval (0, 1].</span>
<span class="sd"> (default: 0.1)</span>
<span class="sd"> maxDepth : int, optional</span>
<span class="sd"> Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1</span>
<span class="sd"> means 1 internal node + 2 leaf nodes).</span>
<span class="sd"> (default: 3)</span>
<span class="sd"> maxBins : int, optional</span>
<span class="sd"> Maximum number of bins used for splitting features. DecisionTree</span>
<span class="sd"> requires maxBins &gt;= max categories.</span>
<span class="sd"> (default: 32)</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`GradientBoostedTreesModel`</span>
<span class="sd"> that can be used for prediction.</span>
<span class="sd"> Examples</span>
<span class="sd"> --------</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.regression import LabeledPoint</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.tree import GradientBoostedTrees</span>
<span class="sd"> &gt;&gt;&gt; from pyspark.mllib.linalg import SparseVector</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; sparse_data = [</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),</span>
<span class="sd"> ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),</span>
<span class="sd"> ... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))</span>
<span class="sd"> ... ]</span>
<span class="sd"> &gt;&gt;&gt;</span>
<span class="sd"> &gt;&gt;&gt; data = sc.parallelize(sparse_data)</span>
<span class="sd"> &gt;&gt;&gt; model = GradientBoostedTrees.trainRegressor(data, {}, numIterations=10)</span>
<span class="sd"> &gt;&gt;&gt; model.numTrees()</span>
<span class="sd"> 10</span>
<span class="sd"> &gt;&gt;&gt; model.totalNumNodes()</span>
<span class="sd"> 12</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {1: 1.0}))</span>
<span class="sd"> 1.0</span>
<span class="sd"> &gt;&gt;&gt; model.predict(SparseVector(2, {0: 1.0}))</span>
<span class="sd"> 0.0</span>
<span class="sd"> &gt;&gt;&gt; rdd = sc.parallelize([[0.0, 1.0], [1.0, 0.0]])</span>
<span class="sd"> &gt;&gt;&gt; model.predict(rdd).collect()</span>
<span class="sd"> [1.0, 0.0]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_train</span><span class="p">(</span>
<span class="n">data</span><span class="p">,</span>
<span class="s2">&quot;regression&quot;</span><span class="p">,</span>
<span class="n">categoricalFeaturesInfo</span><span class="p">,</span>
<span class="n">loss</span><span class="p">,</span>
<span class="n">numIterations</span><span class="p">,</span>
<span class="n">learningRate</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="p">,</span>
<span class="n">maxBins</span><span class="p">,</span>
<span class="p">)</span></div></div>
<span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span>
<span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;mllib.tree tests&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span>
<span class="n">globs</span><span class="p">[</span><span class="s2">&quot;sc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span>
<span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
<span class="n">_test</span><span class="p">()</span>
</pre></div>
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