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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 "License"); 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 "AS IS" 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">"T"</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">"""</span> |
| <span class="sd"> Abstraction for Decision Tree models.</span> |
| |
| <span class="sd"> .. versionadded:: 1.5.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Return number of nodes of the decision tree."""</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">"numNodes"</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Return depth of the decision tree."""</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">"depth"</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Full description of model."""</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">"toDebugString"</span><span class="p">)</span> |
| |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"3.0.0"</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Predict the indices of the leaves corresponding to the feature vector.</span> |
| <span class="sd"> """</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">"predictLeaf"</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">"""</span> |
| <span class="sd"> Mixin for Decision Tree parameters.</span> |
| <span class="sd"> """</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">"leafCol"</span><span class="p">,</span> |
| <span class="s2">"Leaf indices column name. Predicted leaf "</span> |
| <span class="o">+</span> <span class="s2">"index of each instance in each tree by preorder."</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">"maxDepth"</span><span class="p">,</span> |
| <span class="s2">"Maximum depth of the tree. (>= 0) E.g., "</span> |
| <span class="o">+</span> <span class="s2">"depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. "</span> |
| <span class="o">+</span> <span class="s2">"Must be in range [0, 30]."</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">"maxBins"</span><span class="p">,</span> |
| <span class="s2">"Max number of bins for discretizing continuous "</span> |
| <span class="o">+</span> <span class="s2">"features. Must be >=2 and >= number of categories for any categorical "</span> |
| <span class="o">+</span> <span class="s2">"feature."</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">"minInstancesPerNode"</span><span class="p">,</span> |
| <span class="s2">"Minimum number of "</span> |
| <span class="o">+</span> <span class="s2">"instances each child must have after split. If a split causes "</span> |
| <span class="o">+</span> <span class="s2">"the left or right child to have fewer than "</span> |
| <span class="o">+</span> <span class="s2">"minInstancesPerNode, the split will be discarded as invalid. "</span> |
| <span class="o">+</span> <span class="s2">"Should be >= 1."</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">"minWeightFractionPerNode"</span><span class="p">,</span> |
| <span class="s2">"Minimum "</span> |
| <span class="s2">"fraction of the weighted sample count that each child "</span> |
| <span class="s2">"must have after split. If a split causes the fraction "</span> |
| <span class="s2">"of the total weight in the left or right child to be "</span> |
| <span class="s2">"less than minWeightFractionPerNode, the split will be "</span> |
| <span class="s2">"discarded as invalid. Should be in interval [0.0, 0.5)."</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">"minInfoGain"</span><span class="p">,</span> |
| <span class="s2">"Minimum information gain for a split "</span> <span class="o">+</span> <span class="s2">"to be considered at a tree node."</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">"maxMemoryInMB"</span><span class="p">,</span> |
| <span class="s2">"Maximum memory in MB allocated to "</span> |
| <span class="o">+</span> <span class="s2">"histogram aggregation. If too small, then 1 node will be split per "</span> |
| <span class="o">+</span> <span class="s2">"iteration, and its aggregates may exceed this size."</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">"cacheNodeIds"</span><span class="p">,</span> |
| <span class="s2">"If false, the algorithm will pass "</span> |
| <span class="o">+</span> <span class="s2">"trees to executors to match instances with nodes. If true, the "</span> |
| <span class="o">+</span> <span class="s2">"algorithm will cache node IDs for each instance. Caching can speed "</span> |
| <span class="o">+</span> <span class="s2">"up training of deeper trees. Users can set how often should the cache "</span> |
| <span class="o">+</span> <span class="s2">"be checkpointed or disable it by setting checkpointInterval."</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">-></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">"P"</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">-></span> <span class="s2">"P"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Sets the value of :py:attr:`leafCol`.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of leafCol or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of maxDepth or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of maxBins or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of minInstancesPerNode or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of minWeightFractionPerNode or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of minInfoGain or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of maxMemoryInMB or its default value.</span> |
| <span class="sd"> """</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">-></span> <span class="nb">bool</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of cacheNodeIds or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> (private abstraction)</span> |
| <span class="sd"> Represents a tree ensemble model.</span> |
| <span class="sd"> """</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</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">-></span> <span class="n">Sequence</span><span class="p">[</span><span class="s2">"_DecisionTreeModel"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""Trees in this ensemble. Warning: These have null parent Estimators."""</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">"trees"</span><span class="p">))]</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Number of trees in ensemble."""</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">"getNumTrees"</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.5.0"</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">-></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">"""Return the weights for each tree"""</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">"javaTreeWeights"</span><span class="p">))</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Total number of nodes, summed over all trees in the ensemble."""</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">"totalNumNodes"</span><span class="p">)</span> |
| |
| <span class="nd">@property</span> <span class="c1"># type: ignore[misc]</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""Full description of model."""</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">"toDebugString"</span><span class="p">)</span> |
| |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"3.0.0"</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Predict the indices of the leaves corresponding to the feature vector.</span> |
| <span class="sd"> """</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">"predictLeaf"</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">"""</span> |
| <span class="sd"> Mixin for Decision Tree-based ensemble algorithms parameters.</span> |
| <span class="sd"> """</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">"subsamplingRate"</span><span class="p">,</span> |
| <span class="s2">"Fraction of the training data "</span> <span class="o">+</span> <span class="s2">"used for learning each decision tree, in range (0, 1]."</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">"auto"</span><span class="p">,</span> <span class="s2">"all"</span><span class="p">,</span> <span class="s2">"onethird"</span><span class="p">,</span> <span class="s2">"sqrt"</span><span class="p">,</span> <span class="s2">"log2"</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">"featureSubsetStrategy"</span><span class="p">,</span> |
| <span class="s2">"The number of features to consider for splits at each tree node. Supported "</span> |
| <span class="o">+</span> <span class="s2">"options: 'auto' (choose automatically for task: If numTrees == 1, set to "</span> |
| <span class="o">+</span> <span class="s2">"'all'. If numTrees > 1 (forest), set to 'sqrt' for classification and to "</span> |
| <span class="o">+</span> <span class="s2">"'onethird' for regression), 'all' (use all features), 'onethird' (use "</span> |
| <span class="o">+</span> <span class="s2">"1/3 of the features), 'sqrt' (use sqrt(number of features)), 'log2' (use "</span> |
| <span class="o">+</span> <span class="s2">"log2(number of features)), 'n' (when n is in the range (0, 1.0], use "</span> |
| <span class="o">+</span> <span class="s2">"n * number of features. When n is in the range (1, number of features), use"</span> |
| <span class="o">+</span> <span class="s2">" n features). default = 'auto'"</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">-></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">"1.4.0"</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of subsamplingRate or its default value.</span> |
| <span class="sd"> """</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">"1.4.0"</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of featureSubsetStrategy or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> Private class to track supported random forest parameters.</span> |
| <span class="sd"> """</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">"numTrees"</span><span class="p">,</span> |
| <span class="s2">"Number of trees to train (>= 1)."</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">"bootstrap"</span><span class="p">,</span> |
| <span class="s2">"Whether bootstrap samples are used "</span> <span class="s2">"when building trees."</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">-></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">"1.4.0"</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">-></span> <span class="nb">int</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of numTrees or its default value.</span> |
| <span class="sd"> """</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">"3.0.0"</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">-></span> <span class="nb">bool</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of bootstrap or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> Private class to track supported GBT params.</span> |
| <span class="sd"> """</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">"stepSize"</span><span class="p">,</span> |
| <span class="s2">"Step size (a.k.a. learning rate) in interval (0, 1] for shrinking "</span> |
| <span class="o">+</span> <span class="s2">"the contribution of each estimator."</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">"validationTol"</span><span class="p">,</span> |
| <span class="s2">"Threshold for stopping early when fit with validation is used. "</span> |
| <span class="o">+</span> <span class="s2">"If the error rate on the validation input changes by less than the "</span> |
| <span class="o">+</span> <span class="s2">"validationTol, then learning will stop early (before `maxIter`). "</span> |
| <span class="o">+</span> <span class="s2">"This parameter is ignored when fit without validation is used."</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">"3.0.0"</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">-></span> <span class="nb">float</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of validationTol or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> Private class to track supported impurity measures.</span> |
| <span class="sd"> """</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">"variance"</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">"impurity"</span><span class="p">,</span> |
| <span class="s2">"Criterion used for information gain calculation (case-insensitive). "</span> |
| <span class="o">+</span> <span class="s2">"Supported options: "</span> |
| <span class="o">+</span> <span class="s2">", "</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">-></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">"1.4.0"</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of impurity or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> Private class to track supported impurity measures.</span> |
| |
| <span class="sd"> .. versionadded:: 1.4.0</span> |
| <span class="sd"> """</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">"entropy"</span><span class="p">,</span> <span class="s2">"gini"</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">"impurity"</span><span class="p">,</span> |
| <span class="s2">"Criterion used for information gain calculation (case-insensitive). "</span> |
| <span class="o">+</span> <span class="s2">"Supported options: "</span> |
| <span class="o">+</span> <span class="s2">", "</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">-></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">"1.6.0"</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">-></span> <span class="nb">str</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Gets the value of impurity or its default value.</span> |
| <span class="sd"> """</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">"""</span> |
| <span class="sd"> Private class to track supported impurity measures.</span> |
| <span class="sd"> """</span> |
| |
| <span class="k">pass</span> |
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