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<h1>Source code for pyspark.ml.param.shared</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements. See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the "License"); you may not use this file except in compliance with</span>
<span class="c"># the License. You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c"># http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an "AS IS" BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</span>
<span class="c"># DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param</span> <span class="kn">import</span> <span class="n">Param</span><span class="p">,</span> <span class="n">Params</span>
<span class="k">class</span> <span class="nc">HasMaxIter</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param maxIter: max number of iterations (>= 0).</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">maxIter</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="s">"maxIter"</span><span class="p">,</span> <span class="s">"max number of iterations (>= 0)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasMaxIter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for max number of iterations (>= 0)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxIter</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"maxIter"</span><span class="p">,</span> <span class="s">"max number of iterations (>= 0)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setMaxIter</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`maxIter`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">maxIter</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getMaxIter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of maxIter 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">maxIter</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasRegParam</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param regParam: regularization parameter (>= 0).</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">regParam</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="s">"regParam"</span><span class="p">,</span> <span class="s">"regularization parameter (>= 0)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasRegParam</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for regularization parameter (>= 0)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">regParam</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"regParam"</span><span class="p">,</span> <span class="s">"regularization parameter (>= 0)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setRegParam</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`regParam`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">regParam</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getRegParam</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of regParam 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">regParam</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasFeaturesCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param featuresCol: features column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">featuresCol</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="s">"featuresCol"</span><span class="p">,</span> <span class="s">"features column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for features column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">featuresCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"featuresCol"</span><span class="p">,</span> <span class="s">"features column name"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">'features'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setFeaturesCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`featuresCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">featuresCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getFeaturesCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of featuresCol 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">featuresCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasLabelCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param labelCol: label column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">labelCol</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="s">"labelCol"</span><span class="p">,</span> <span class="s">"label column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasLabelCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for label column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">labelCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"labelCol"</span><span class="p">,</span> <span class="s">"label column name"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">'label'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setLabelCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`labelCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">labelCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getLabelCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of labelCol 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">labelCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasPredictionCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param predictionCol: prediction column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">predictionCol</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="s">"predictionCol"</span><span class="p">,</span> <span class="s">"prediction column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasPredictionCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for prediction column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">predictionCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"predictionCol"</span><span class="p">,</span> <span class="s">"prediction column name"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="s">'prediction'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setPredictionCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`predictionCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">predictionCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getPredictionCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of predictionCol 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">predictionCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasProbabilityCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param probabilityCol: Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities..</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">probabilityCol</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="s">"probabilityCol"</span><span class="p">,</span> <span class="s">"Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasProbabilityCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">probabilityCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"probabilityCol"</span><span class="p">,</span> <span class="s">"Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities."</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">probabilityCol</span><span class="o">=</span><span class="s">'probability'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setProbabilityCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`probabilityCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">probabilityCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getProbabilityCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of probabilityCol 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">probabilityCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasRawPredictionCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param rawPredictionCol: raw prediction (a.k.a. confidence) column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">rawPredictionCol</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="s">"rawPredictionCol"</span><span class="p">,</span> <span class="s">"raw prediction (a.k.a. confidence) column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasRawPredictionCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for raw prediction (a.k.a. confidence) column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rawPredictionCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"rawPredictionCol"</span><span class="p">,</span> <span class="s">"raw prediction (a.k.a. confidence) column name"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">rawPredictionCol</span><span class="o">=</span><span class="s">'rawPrediction'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setRawPredictionCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`rawPredictionCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">rawPredictionCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getRawPredictionCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of rawPredictionCol 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">rawPredictionCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasInputCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param inputCol: input column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">inputCol</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="s">"inputCol"</span><span class="p">,</span> <span class="s">"input column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasInputCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for input column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"inputCol"</span><span class="p">,</span> <span class="s">"input column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setInputCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`inputCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">inputCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getInputCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of inputCol 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">inputCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasInputCols</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param inputCols: input column names.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">inputCols</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="s">"inputCols"</span><span class="p">,</span> <span class="s">"input column names"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasInputCols</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for input column names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">inputCols</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"inputCols"</span><span class="p">,</span> <span class="s">"input column names"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setInputCols</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`inputCols`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">inputCols</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getInputCols</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of inputCols 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">inputCols</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasOutputCol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param outputCol: output column name.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">outputCol</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="s">"outputCol"</span><span class="p">,</span> <span class="s">"output column name"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasOutputCol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for output column name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">outputCol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"outputCol"</span><span class="p">,</span> <span class="s">"output column name"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">outputCol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">uid</span> <span class="o">+</span> <span class="s">'__output'</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setOutputCol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`outputCol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">outputCol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getOutputCol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of outputCol 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">outputCol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasNumFeatures</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param numFeatures: number of features.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">numFeatures</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="s">"numFeatures"</span><span class="p">,</span> <span class="s">"number of features"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasNumFeatures</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for number of features</span>
<span class="bp">self</span><span class="o">.</span><span class="n">numFeatures</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"numFeatures"</span><span class="p">,</span> <span class="s">"number of features"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setNumFeatures</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`numFeatures`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">numFeatures</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getNumFeatures</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of numFeatures 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">numFeatures</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasCheckpointInterval</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param checkpointInterval: checkpoint interval (>= 1).</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">checkpointInterval</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="s">"checkpointInterval"</span><span class="p">,</span> <span class="s">"checkpoint interval (>= 1)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasCheckpointInterval</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for checkpoint interval (>= 1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">checkpointInterval</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"checkpointInterval"</span><span class="p">,</span> <span class="s">"checkpoint interval (>= 1)"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setCheckpointInterval</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`checkpointInterval`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointInterval</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getCheckpointInterval</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of checkpointInterval 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">checkpointInterval</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasSeed</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param seed: random seed.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">seed</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="s">"seed"</span><span class="p">,</span> <span class="s">"random seed"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasSeed</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for random seed</span>
<span class="bp">self</span><span class="o">.</span><span class="n">seed</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"seed"</span><span class="p">,</span> <span class="s">"random seed"</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="nb">hash</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__name__</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">setSeed</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`seed`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getSeed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of seed 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">seed</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasTol</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param tol: the convergence tolerance for iterative algorithms.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">tol</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="s">"tol"</span><span class="p">,</span> <span class="s">"the convergence tolerance for iterative algorithms"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasTol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for the convergence tolerance for iterative algorithms</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tol</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"tol"</span><span class="p">,</span> <span class="s">"the convergence tolerance for iterative algorithms"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setTol</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`tol`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">tol</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getTol</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of tol 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">tol</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">HasStepSize</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for param stepSize: Step size to be used for each iteration of optimization..</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">stepSize</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="s">"stepSize"</span><span class="p">,</span> <span class="s">"Step size to be used for each iteration of optimization."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">HasStepSize</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
<span class="c">#: param for Step size to be used for each iteration of optimization.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stepSize</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"stepSize"</span><span class="p">,</span> <span class="s">"Step size to be used for each iteration of optimization."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setStepSize</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`stepSize`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stepSize</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">getStepSize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Gets the value of stepSize 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">stepSize</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">DecisionTreeParams</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Mixin for Decision Tree parameters.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">maxDepth</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="s">"maxDepth"</span><span class="p">,</span> <span class="s">"Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes."</span><span class="p">)</span>
<span class="n">maxBins</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="s">"maxBins"</span><span class="p">,</span> <span class="s">"Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature."</span><span class="p">)</span>
<span class="n">minInstancesPerNode</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="s">"minInstancesPerNode"</span><span class="p">,</span> <span class="s">"Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1."</span><span class="p">)</span>
<span class="n">minInfoGain</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="s">"minInfoGain"</span><span class="p">,</span> <span class="s">"Minimum information gain for a split to be considered at a tree node."</span><span class="p">)</span>
<span class="n">maxMemoryInMB</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="s">"maxMemoryInMB"</span><span class="p">,</span> <span class="s">"Maximum memory in MB allocated to histogram aggregation."</span><span class="p">)</span>
<span class="n">cacheNodeIds</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="s">"cacheNodeIds"</span><span class="p">,</span> <span class="s">"If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</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="n">__init__</span><span class="p">()</span>
<span class="c">#: param for Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxDepth</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"maxDepth"</span><span class="p">,</span> <span class="s">"Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes."</span><span class="p">)</span>
<span class="c">#: param for Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxBins</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"maxBins"</span><span class="p">,</span> <span class="s">"Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature."</span><span class="p">)</span>
<span class="c">#: param for Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">minInstancesPerNode</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"minInstancesPerNode"</span><span class="p">,</span> <span class="s">"Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Should be >= 1."</span><span class="p">)</span>
<span class="c">#: param for Minimum information gain for a split to be considered at a tree node.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">minInfoGain</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"minInfoGain"</span><span class="p">,</span> <span class="s">"Minimum information gain for a split to be considered at a tree node."</span><span class="p">)</span>
<span class="c">#: param for Maximum memory in MB allocated to histogram aggregation.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">maxMemoryInMB</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"maxMemoryInMB"</span><span class="p">,</span> <span class="s">"Maximum memory in MB allocated to histogram aggregation."</span><span class="p">)</span>
<span class="c">#: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cacheNodeIds</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">"cacheNodeIds"</span><span class="p">,</span> <span class="s">"If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees."</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setMaxDepth</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`maxDepth`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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">setMaxBins</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`maxBins`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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">setMinInstancesPerNode</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`minInstancesPerNode`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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">setMinInfoGain</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`minInfoGain`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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">setMaxMemoryInMB</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`maxMemoryInMB`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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">setCacheNodeIds</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="sd">"""</span>
<span class="sd"> Sets the value of :py:attr:`cacheNodeIds`.</span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</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="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</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="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>
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