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<h1>Source code for pyspark.ml.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">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">TypeVar</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">since</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.linalg</span> <span class="kn">import</span> <span class="n">Vector</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param</span> <span class="kn">import</span> <span class="n">Params</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param.shared</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">HasCheckpointInterval</span><span class="p">,</span>
<span class="n">HasSeed</span><span class="p">,</span>
<span class="n">HasWeightCol</span><span class="p">,</span>
<span class="n">Param</span><span class="p">,</span>
<span class="n">TypeConverters</span><span class="p">,</span>
<span class="n">HasMaxIter</span><span class="p">,</span>
<span class="n">HasStepSize</span><span class="p">,</span>
<span class="n">HasValidationIndicatorCol</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.wrapper</span> <span class="kn">import</span> <span class="n">JavaPredictionModel</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.common</span> <span class="kn">import</span> <span class="n">inherit_doc</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.ml._typing</span> <span class="kn">import</span> <span class="n">P</span>
<span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">&quot;T&quot;</span><span class="p">)</span>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">_DecisionTreeModel</span><span class="p">(</span><span class="n">JavaPredictionModel</span><span class="p">[</span><span class="n">T</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstraction for Decision Tree models.</span>
<span class="sd"> .. versionadded:: 1.5.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.5.0&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;Return number of nodes of the decision tree.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;numNodes&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.5.0&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;Return depth of the decision tree.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;depth&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&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 description of model.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;toDebugString&quot;</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">predictLeaf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Vector</span><span class="p">)</span> <span class="o">-&gt;</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 indices of the leaves corresponding to the feature vector.</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_java</span><span class="p">(</span><span class="s2">&quot;predictLeaf&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_DecisionTreeParams</span><span class="p">(</span><span class="n">HasCheckpointInterval</span><span class="p">,</span> <span class="n">HasSeed</span><span class="p">,</span> <span class="n">HasWeightCol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Mixin for Decision Tree parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">leafCol</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;leafCol&quot;</span><span class="p">,</span>
<span class="s2">&quot;Leaf indices column name. Predicted leaf &quot;</span>
<span class="o">+</span> <span class="s2">&quot;index of each instance in each tree by preorder.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">maxDepth</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;maxDepth&quot;</span><span class="p">,</span>
<span class="s2">&quot;Maximum depth of the tree. (&gt;= 0) E.g., &quot;</span>
<span class="o">+</span> <span class="s2">&quot;depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. &quot;</span>
<span class="o">+</span> <span class="s2">&quot;Must be in range [0, 30].&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">maxBins</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;maxBins&quot;</span><span class="p">,</span>
<span class="s2">&quot;Max number of bins for discretizing continuous &quot;</span>
<span class="o">+</span> <span class="s2">&quot;features. Must be &gt;=2 and &gt;= number of categories for any categorical &quot;</span>
<span class="o">+</span> <span class="s2">&quot;feature.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">minInstancesPerNode</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;minInstancesPerNode&quot;</span><span class="p">,</span>
<span class="s2">&quot;Minimum number of &quot;</span>
<span class="o">+</span> <span class="s2">&quot;instances each child must have after split. If a split causes &quot;</span>
<span class="o">+</span> <span class="s2">&quot;the left or right child to have fewer than &quot;</span>
<span class="o">+</span> <span class="s2">&quot;minInstancesPerNode, the split will be discarded as invalid. &quot;</span>
<span class="o">+</span> <span class="s2">&quot;Should be &gt;= 1.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">minWeightFractionPerNode</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;minWeightFractionPerNode&quot;</span><span class="p">,</span>
<span class="s2">&quot;Minimum &quot;</span>
<span class="s2">&quot;fraction of the weighted sample count that each child &quot;</span>
<span class="s2">&quot;must have after split. If a split causes the fraction &quot;</span>
<span class="s2">&quot;of the total weight in the left or right child to be &quot;</span>
<span class="s2">&quot;less than minWeightFractionPerNode, the split will be &quot;</span>
<span class="s2">&quot;discarded as invalid. Should be in interval [0.0, 0.5).&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">minInfoGain</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;minInfoGain&quot;</span><span class="p">,</span>
<span class="s2">&quot;Minimum information gain for a split &quot;</span> <span class="o">+</span> <span class="s2">&quot;to be considered at a tree node.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">maxMemoryInMB</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;maxMemoryInMB&quot;</span><span class="p">,</span>
<span class="s2">&quot;Maximum memory in MB allocated to &quot;</span>
<span class="o">+</span> <span class="s2">&quot;histogram aggregation. If too small, then 1 node will be split per &quot;</span>
<span class="o">+</span> <span class="s2">&quot;iteration, and its aggregates may exceed this size.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">cacheNodeIds</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;cacheNodeIds&quot;</span><span class="p">,</span>
<span class="s2">&quot;If false, the algorithm will pass &quot;</span>
<span class="o">+</span> <span class="s2">&quot;trees to executors to match instances with nodes. If true, the &quot;</span>
<span class="o">+</span> <span class="s2">&quot;algorithm will cache node IDs for each instance. Caching can speed &quot;</span>
<span class="o">+</span> <span class="s2">&quot;up training of deeper trees. Users can set how often should the cache &quot;</span>
<span class="o">+</span> <span class="s2">&quot;be checkpointed or disable it by setting checkpointInterval.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toBoolean</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_DecisionTreeParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">setLeafCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="s2">&quot;P&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;P&quot;</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`leafCol`.</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">_set</span><span class="p">(</span><span class="n">leafCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getLeafCol</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;</span>
<span class="sd"> Gets the value of leafCol or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">leafCol</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMaxDepth</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"> Gets the value of maxDepth or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">maxDepth</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMaxBins</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"> Gets the value of maxBins or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">maxBins</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMinInstancesPerNode</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"> Gets the value of minInstancesPerNode or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">minInstancesPerNode</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMinWeightFractionPerNode</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of minWeightFractionPerNode or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">minWeightFractionPerNode</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMinInfoGain</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of minInfoGain or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">minInfoGain</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getMaxMemoryInMB</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"> Gets the value of maxMemoryInMB or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">maxMemoryInMB</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getCacheNodeIds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of cacheNodeIds or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cacheNodeIds</span><span class="p">)</span>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">_TreeEnsembleModel</span><span class="p">(</span><span class="n">JavaPredictionModel</span><span class="p">[</span><span class="n">T</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> (private abstraction)</span>
<span class="sd"> Represents a tree ensemble model.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">trees</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Sequence</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;Trees in this ensemble. Warning: These have null parent Estimators.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">[</span><span class="n">_DecisionTreeModel</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;trees&quot;</span><span class="p">))]</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getNumTrees</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;Number of trees in ensemble.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;getNumTrees&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.5.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">treeWeights</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Return the weights for each tree&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;javaTreeWeights&quot;</span><span class="p">))</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&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;Total number of nodes, summed over all trees in the ensemble.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;totalNumNodes&quot;</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&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 description of model.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;toDebugString&quot;</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">predictLeaf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">Vector</span><span class="p">)</span> <span class="o">-&gt;</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 indices of the leaves corresponding to the feature vector.</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_java</span><span class="p">(</span><span class="s2">&quot;predictLeaf&quot;</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_TreeEnsembleParams</span><span class="p">(</span><span class="n">_DecisionTreeParams</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Mixin for Decision Tree-based ensemble algorithms parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">subsamplingRate</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;subsamplingRate&quot;</span><span class="p">,</span>
<span class="s2">&quot;Fraction of the training data &quot;</span> <span class="o">+</span> <span class="s2">&quot;used for learning each decision tree, in range (0, 1].&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">supportedFeatureSubsetStrategies</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</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;onethird&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="n">featureSubsetStrategy</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;featureSubsetStrategy&quot;</span><span class="p">,</span>
<span class="s2">&quot;The number of features to consider for splits at each tree node. Supported &quot;</span>
<span class="o">+</span> <span class="s2">&quot;options: &#39;auto&#39; (choose automatically for task: If numTrees == 1, set to &quot;</span>
<span class="o">+</span> <span class="s2">&quot;&#39;all&#39;. If numTrees &gt; 1 (forest), set to &#39;sqrt&#39; for classification and to &quot;</span>
<span class="o">+</span> <span class="s2">&quot;&#39;onethird&#39; for regression), &#39;all&#39; (use all features), &#39;onethird&#39; (use &quot;</span>
<span class="o">+</span> <span class="s2">&quot;1/3 of the features), &#39;sqrt&#39; (use sqrt(number of features)), &#39;log2&#39; (use &quot;</span>
<span class="o">+</span> <span class="s2">&quot;log2(number of features)), &#39;n&#39; (when n is in the range (0, 1.0], use &quot;</span>
<span class="o">+</span> <span class="s2">&quot;n * number of features. When n is in the range (1, number of features), use&quot;</span>
<span class="o">+</span> <span class="s2">&quot; n features). default = &#39;auto&#39;&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_TreeEnsembleParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getSubsamplingRate</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of subsamplingRate or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">subsamplingRate</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getFeatureSubsetStrategy</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;</span>
<span class="sd"> Gets the value of featureSubsetStrategy or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">featureSubsetStrategy</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_RandomForestParams</span><span class="p">(</span><span class="n">_TreeEnsembleParams</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Private class to track supported random forest parameters.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">numTrees</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;numTrees&quot;</span><span class="p">,</span>
<span class="s2">&quot;Number of trees to train (&gt;= 1).&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toInt</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">bootstrap</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;bootstrap&quot;</span><span class="p">,</span>
<span class="s2">&quot;Whether bootstrap samples are used &quot;</span> <span class="s2">&quot;when building trees.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toBoolean</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_RandomForestParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getNumTrees</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"> Gets the value of numTrees or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">numTrees</span><span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getBootstrap</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of bootstrap or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_GBTParams</span><span class="p">(</span><span class="n">_TreeEnsembleParams</span><span class="p">,</span> <span class="n">HasMaxIter</span><span class="p">,</span> <span class="n">HasStepSize</span><span class="p">,</span> <span class="n">HasValidationIndicatorCol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Private class to track supported GBT params.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">stepSize</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;stepSize&quot;</span><span class="p">,</span>
<span class="s2">&quot;Step size (a.k.a. learning rate) in interval (0, 1] for shrinking &quot;</span>
<span class="o">+</span> <span class="s2">&quot;the contribution of each estimator.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">validationTol</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;validationTol&quot;</span><span class="p">,</span>
<span class="s2">&quot;Threshold for stopping early when fit with validation is used. &quot;</span>
<span class="o">+</span> <span class="s2">&quot;If the error rate on the validation input changes by less than the &quot;</span>
<span class="o">+</span> <span class="s2">&quot;validationTol, then learning will stop early (before `maxIter`). &quot;</span>
<span class="o">+</span> <span class="s2">&quot;This parameter is ignored when fit without validation is used.&quot;</span><span class="p">,</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">,</span>
<span class="p">)</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getValidationTol</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the value of validationTol or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">validationTol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_HasVarianceImpurity</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Private class to track supported impurity measures.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">supportedImpurities</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;variance&quot;</span><span class="p">]</span>
<span class="n">impurity</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;impurity&quot;</span><span class="p">,</span>
<span class="s2">&quot;Criterion used for information gain calculation (case-insensitive). &quot;</span>
<span class="o">+</span> <span class="s2">&quot;Supported options: &quot;</span>
<span class="o">+</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">supportedImpurities</span><span class="p">),</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_HasVarianceImpurity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getImpurity</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;</span>
<span class="sd"> Gets the value of impurity or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">impurity</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_TreeClassifierParams</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Private class to track supported impurity measures.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">supportedImpurities</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;entropy&quot;</span><span class="p">,</span> <span class="s2">&quot;gini&quot;</span><span class="p">]</span>
<span class="n">impurity</span><span class="p">:</span> <span class="n">Param</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span>
<span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span>
<span class="s2">&quot;impurity&quot;</span><span class="p">,</span>
<span class="s2">&quot;Criterion used for information gain calculation (case-insensitive). &quot;</span>
<span class="o">+</span> <span class="s2">&quot;Supported options: &quot;</span>
<span class="o">+</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">supportedImpurities</span><span class="p">),</span>
<span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">super</span><span class="p">(</span><span class="n">_TreeClassifierParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.6.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">getImpurity</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;</span>
<span class="sd"> Gets the value of impurity or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">impurity</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_TreeRegressorParams</span><span class="p">(</span><span class="n">_HasVarianceImpurity</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Private class to track supported impurity measures.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span>
</pre></div>
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