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<h1>Source code for pyspark.ml.regression</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="kn">from</span> <span class="nn">pyspark.ml.util</span> <span class="kn">import</span> <span class="n">keyword_only</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.wrapper</span> <span class="kn">import</span> <span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">JavaModel</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param.shared</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">inherit_doc</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">'DecisionTreeRegressor'</span><span class="p">,</span> <span class="s">'DecisionTreeRegressionModel'</span><span class="p">,</span> <span class="s">'GBTRegressor'</span><span class="p">,</span>
<span class="s">'GBTRegressionModel'</span><span class="p">,</span> <span class="s">'LinearRegression'</span><span class="p">,</span> <span class="s">'LinearRegressionModel'</span><span class="p">,</span>
<span class="s">'RandomForestRegressor'</span><span class="p">,</span> <span class="s">'RandomForestRegressionModel'</span><span class="p">]</span>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="LinearRegression"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.LinearRegression">[docs]</a><span class="k">class</span> <span class="nc">LinearRegression</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasMaxIter</span><span class="p">,</span>
<span class="n">HasRegParam</span><span class="p">,</span> <span class="n">HasTol</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Linear regression.</span>
<span class="sd"> The learning objective is to minimize the squared error, with regularization.</span>
<span class="sd"> The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^</span>
<span class="sd"> This support multiple types of regularization:</span>
<span class="sd"> - none (a.k.a. ordinary least squares)</span>
<span class="sd"> - L2 (ridge regression)</span>
<span class="sd"> - L1 (Lasso)</span>
<span class="sd"> - L2 + L1 (elastic net)</span>
<span class="sd"> >>> from pyspark.mllib.linalg import Vectors</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([</span>
<span class="sd"> ... (1.0, Vectors.dense(1.0)),</span>
<span class="sd"> ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])</span>
<span class="sd"> >>> lr = LinearRegression(maxIter=5, regParam=0.0)</span>
<span class="sd"> >>> model = lr.fit(df)</span>
<span class="sd"> >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])</span>
<span class="sd"> >>> model.transform(test0).head().prediction</span>
<span class="sd"> -1.0</span>
<span class="sd"> >>> model.weights</span>
<span class="sd"> DenseVector([1.0])</span>
<span class="sd"> >>> model.intercept</span>
<span class="sd"> 0.0</span>
<span class="sd"> >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])</span>
<span class="sd"> >>> model.transform(test1).head().prediction</span>
<span class="sd"> 1.0</span>
<span class="sd"> >>> lr.setParams("vector")</span>
<span class="sd"> Traceback (most recent call last):</span>
<span class="sd"> ...</span>
<span class="sd"> TypeError: Method setParams forces keyword arguments.</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">elasticNetParam</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">"elasticNetParam"</span><span class="p">,</span>
<span class="s">"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, "</span> <span class="o">+</span>
<span class="s">"the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty."</span><span class="p">)</span>
<span class="nd">@keyword_only</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="n">featuresCol</span><span class="o">=</span><span class="s">"features"</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="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span>
<span class="n">maxIter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">elasticNetParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">LinearRegression</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="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
<span class="s">"org.apache.spark.ml.regression.LinearRegression"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
<span class="c">#: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty</span>
<span class="c"># is an L2 penalty. For alpha = 1, it is an L1 penalty.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">elasticNetParam</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">"elasticNetParam"</span><span class="p">,</span>
<span class="s">"the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty "</span> <span class="o">+</span>
<span class="s">"is an L2 penalty. For alpha = 1, it is an L1 penalty."</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">maxIter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">elasticNetParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">)</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@keyword_only</span>
<div class="viewcode-block" id="LinearRegression.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.LinearRegression.setParams">[docs]</a> <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</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="n">labelCol</span><span class="o">=</span><span class="s">"label"</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="n">maxIter</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">elasticNetParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)</span>
<span class="sd"> Sets params for linear regression.</span>
<span class="sd"> """</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<span class="k">def</span> <span class="nf">_create_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">java_model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">LinearRegressionModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
<div class="viewcode-block" id="LinearRegression.setElasticNetParam"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.LinearRegression.setElasticNetParam">[docs]</a> <span class="k">def</span> <span class="nf">setElasticNetParam</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:`elasticNetParam`.</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">elasticNetParam</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="LinearRegression.getElasticNetParam"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.LinearRegression.getElasticNetParam">[docs]</a> <span class="k">def</span> <span class="nf">getElasticNetParam</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 elasticNetParam 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">elasticNetParam</span><span class="p">)</span>
</div></div>
<div class="viewcode-block" id="LinearRegressionModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.LinearRegressionModel">[docs]</a><span class="k">class</span> <span class="nc">LinearRegressionModel</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model fitted by LinearRegression.</span>
<span class="sd"> """</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model weights.</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="s">"weights"</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">intercept</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model intercept.</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="s">"intercept"</span><span class="p">)</span>
</div>
<span class="k">class</span> <span class="nc">TreeRegressorParams</span><span class="p">(</span><span class="nb">object</span><span class="p">):</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="o">=</span> <span class="p">[</span><span class="s">"variance"</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">RandomForestParams</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Private class to track supported random forest parameters.</span>
<span class="sd"> """</span>
<span class="n">supportedFeatureSubsetStrategies</span> <span class="o">=</span> <span class="p">[</span><span class="s">"auto"</span><span class="p">,</span> <span class="s">"all"</span><span class="p">,</span> <span class="s">"onethird"</span><span class="p">,</span> <span class="s">"sqrt"</span><span class="p">,</span> <span class="s">"log2"</span><span class="p">]</span>
<span class="k">class</span> <span class="nc">GBTParams</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Private class to track supported GBT params.</span>
<span class="sd"> """</span>
<span class="n">supportedLossTypes</span> <span class="o">=</span> <span class="p">[</span><span class="s">"squared"</span><span class="p">,</span> <span class="s">"absolute"</span><span class="p">]</span>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="DecisionTreeRegressor"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor">[docs]</a><span class="k">class</span> <span class="nc">DecisionTreeRegressor</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span>
<span class="n">DecisionTreeParams</span><span class="p">,</span> <span class="n">HasCheckpointInterval</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`</span>
<span class="sd"> learning algorithm for regression.</span>
<span class="sd"> It supports both continuous and categorical features.</span>
<span class="sd"> >>> from pyspark.mllib.linalg import Vectors</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([</span>
<span class="sd"> ... (1.0, Vectors.dense(1.0)),</span>
<span class="sd"> ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])</span>
<span class="sd"> >>> dt = DecisionTreeRegressor(maxDepth=2)</span>
<span class="sd"> >>> model = dt.fit(df)</span>
<span class="sd"> >>> model.depth</span>
<span class="sd"> 1</span>
<span class="sd"> >>> model.numNodes</span>
<span class="sd"> 3</span>
<span class="sd"> >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])</span>
<span class="sd"> >>> model.transform(test0).head().prediction</span>
<span class="sd"> 0.0</span>
<span class="sd"> >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])</span>
<span class="sd"> >>> model.transform(test1).head().prediction</span>
<span class="sd"> 1.0</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">impurity</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">"impurity"</span><span class="p">,</span>
<span class="s">"Criterion used for information gain calculation (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">TreeRegressorParams</span><span class="o">.</span><span class="n">supportedImpurities</span><span class="p">))</span>
<span class="nd">@keyword_only</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="n">featuresCol</span><span class="o">=</span><span class="s">"features"</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="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">DecisionTreeRegressor</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="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
<span class="s">"org.apache.spark.ml.regression.DecisionTreeRegressor"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
<span class="c">#: param for Criterion used for information gain calculation (case-insensitive).</span>
<span class="bp">self</span><span class="o">.</span><span class="n">impurity</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">"impurity"</span><span class="p">,</span>
<span class="s">"Criterion used for information gain calculation (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">TreeRegressorParams</span><span class="o">.</span><span class="n">supportedImpurities</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">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">)</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@keyword_only</span>
<div class="viewcode-block" id="DecisionTreeRegressor.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor.setParams">[docs]</a> <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</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="n">labelCol</span><span class="o">=</span><span class="s">"label"</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="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance")</span>
<span class="sd"> Sets params for the DecisionTreeRegressor.</span>
<span class="sd"> """</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<span class="k">def</span> <span class="nf">_create_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">java_model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">DecisionTreeRegressionModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
<div class="viewcode-block" id="DecisionTreeRegressor.setImpurity"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor.setImpurity">[docs]</a> <span class="k">def</span> <span class="nf">setImpurity</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:`impurity`.</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">impurity</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="DecisionTreeRegressor.getImpurity"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor.getImpurity">[docs]</a> <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="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>
</div></div>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">DecisionTreeModel</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">):</span>
<span class="nd">@property</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="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="s">"numNodes"</span><span class="p">)</span>
<span class="nd">@property</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="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="s">"depth"</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</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="s">"toString"</span><span class="p">)</span>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">TreeEnsembleModels</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">):</span>
<span class="nd">@property</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="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="s">"javaTreeWeights"</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</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="s">"toString"</span><span class="p">)</span>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="DecisionTreeRegressionModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressionModel">[docs]</a><span class="k">class</span> <span class="nc">DecisionTreeRegressionModel</span><span class="p">(</span><span class="n">DecisionTreeModel</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model fitted by DecisionTreeRegressor.</span>
<span class="sd"> """</span>
</div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="RandomForestRegressor"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor">[docs]</a><span class="k">class</span> <span class="nc">RandomForestRegressor</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasSeed</span><span class="p">,</span>
<span class="n">DecisionTreeParams</span><span class="p">,</span> <span class="n">HasCheckpointInterval</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> `http://en.wikipedia.org/wiki/Random_forest Random Forest`</span>
<span class="sd"> learning algorithm for regression.</span>
<span class="sd"> It supports both continuous and categorical features.</span>
<span class="sd"> >>> from numpy import allclose</span>
<span class="sd"> >>> from pyspark.mllib.linalg import Vectors</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([</span>
<span class="sd"> ... (1.0, Vectors.dense(1.0)),</span>
<span class="sd"> ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])</span>
<span class="sd"> >>> rf = RandomForestRegressor(numTrees=2, maxDepth=2, seed=42)</span>
<span class="sd"> >>> model = rf.fit(df)</span>
<span class="sd"> >>> allclose(model.treeWeights, [1.0, 1.0])</span>
<span class="sd"> True</span>
<span class="sd"> >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])</span>
<span class="sd"> >>> model.transform(test0).head().prediction</span>
<span class="sd"> 0.0</span>
<span class="sd"> >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])</span>
<span class="sd"> >>> model.transform(test1).head().prediction</span>
<span class="sd"> 0.5</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">impurity</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">"impurity"</span><span class="p">,</span>
<span class="s">"Criterion used for information gain calculation (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">TreeRegressorParams</span><span class="o">.</span><span class="n">supportedImpurities</span><span class="p">))</span>
<span class="n">subsamplingRate</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">"subsamplingRate"</span><span class="p">,</span>
<span class="s">"Fraction of the training data used for learning each decision tree, "</span> <span class="o">+</span>
<span class="s">"in range (0, 1]."</span><span class="p">)</span>
<span class="n">numTrees</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">"numTrees"</span><span class="p">,</span> <span class="s">"Number of trees to train (>= 1)"</span><span class="p">)</span>
<span class="n">featureSubsetStrategy</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">"featureSubsetStrategy"</span><span class="p">,</span>
<span class="s">"The number of features to consider for splits at each tree node. Supported "</span> <span class="o">+</span>
<span class="s">"options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">RandomForestParams</span><span class="o">.</span><span class="n">supportedFeatureSubsetStrategies</span><span class="p">))</span>
<span class="nd">@keyword_only</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="n">featuresCol</span><span class="o">=</span><span class="s">"features"</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="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">,</span>
<span class="n">numTrees</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">featureSubsetStrategy</span><span class="o">=</span><span class="s">"auto"</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \</span>
<span class="sd"> impurity="variance", numTrees=20, \</span>
<span class="sd"> featureSubsetStrategy="auto", seed=None)</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">RandomForestRegressor</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="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
<span class="s">"org.apache.spark.ml.regression.RandomForestRegressor"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
<span class="c">#: param for Criterion used for information gain calculation (case-insensitive).</span>
<span class="bp">self</span><span class="o">.</span><span class="n">impurity</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">"impurity"</span><span class="p">,</span>
<span class="s">"Criterion used for information gain calculation (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">TreeRegressorParams</span><span class="o">.</span><span class="n">supportedImpurities</span><span class="p">))</span>
<span class="c">#: param for Fraction of the training data used for learning each decision tree,</span>
<span class="c"># in range (0, 1]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subsamplingRate</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">"subsamplingRate"</span><span class="p">,</span>
<span class="s">"Fraction of the training data used for learning each "</span> <span class="o">+</span>
<span class="s">"decision tree, in range (0, 1]."</span><span class="p">)</span>
<span class="c">#: param for Number of trees to train (>= 1)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">numTrees</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">"numTrees"</span><span class="p">,</span> <span class="s">"Number of trees to train (>= 1)"</span><span class="p">)</span>
<span class="c">#: param for The number of features to consider for splits at each tree node</span>
<span class="bp">self</span><span class="o">.</span><span class="n">featureSubsetStrategy</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">"featureSubsetStrategy"</span><span class="p">,</span>
<span class="s">"The number of features to consider for splits at each tree node. Supported "</span> <span class="o">+</span>
<span class="s">"options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">RandomForestParams</span><span class="o">.</span><span class="n">supportedFeatureSubsetStrategies</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">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">,</span> <span class="n">numTrees</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">featureSubsetStrategy</span><span class="o">=</span><span class="s">"auto"</span><span class="p">)</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@keyword_only</span>
<div class="viewcode-block" id="RandomForestRegressor.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.setParams">[docs]</a> <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</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="n">labelCol</span><span class="o">=</span><span class="s">"label"</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="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
<span class="n">impurity</span><span class="o">=</span><span class="s">"variance"</span><span class="p">,</span> <span class="n">numTrees</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">featureSubsetStrategy</span><span class="o">=</span><span class="s">"auto"</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \</span>
<span class="sd"> impurity="variance", numTrees=20, featureSubsetStrategy="auto")</span>
<span class="sd"> Sets params for linear regression.</span>
<span class="sd"> """</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<span class="k">def</span> <span class="nf">_create_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">java_model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">RandomForestRegressionModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
<div class="viewcode-block" id="RandomForestRegressor.setImpurity"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.setImpurity">[docs]</a> <span class="k">def</span> <span class="nf">setImpurity</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:`impurity`.</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">impurity</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="RandomForestRegressor.getImpurity"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.getImpurity">[docs]</a> <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="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>
</div>
<div class="viewcode-block" id="RandomForestRegressor.setSubsamplingRate"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.setSubsamplingRate">[docs]</a> <span class="k">def</span> <span class="nf">setSubsamplingRate</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:`subsamplingRate`.</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">subsamplingRate</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="RandomForestRegressor.getSubsamplingRate"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.getSubsamplingRate">[docs]</a> <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="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>
</div>
<div class="viewcode-block" id="RandomForestRegressor.setNumTrees"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.setNumTrees">[docs]</a> <span class="k">def</span> <span class="nf">setNumTrees</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:`numTrees`.</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">numTrees</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="RandomForestRegressor.getNumTrees"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.getNumTrees">[docs]</a> <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="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>
</div>
<div class="viewcode-block" id="RandomForestRegressor.setFeatureSubsetStrategy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.setFeatureSubsetStrategy">[docs]</a> <span class="k">def</span> <span class="nf">setFeatureSubsetStrategy</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:`featureSubsetStrategy`.</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">featureSubsetStrategy</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="RandomForestRegressor.getFeatureSubsetStrategy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor.getFeatureSubsetStrategy">[docs]</a> <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="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>
</div></div>
<div class="viewcode-block" id="RandomForestRegressionModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.RandomForestRegressionModel">[docs]</a><span class="k">class</span> <span class="nc">RandomForestRegressionModel</span><span class="p">(</span><span class="n">TreeEnsembleModels</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model fitted by RandomForestRegressor.</span>
<span class="sd"> """</span>
</div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="GBTRegressor"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor">[docs]</a><span class="k">class</span> <span class="nc">GBTRegressor</span><span class="p">(</span><span class="n">JavaEstimator</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasMaxIter</span><span class="p">,</span>
<span class="n">DecisionTreeParams</span><span class="p">,</span> <span class="n">HasCheckpointInterval</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`</span>
<span class="sd"> learning algorithm for regression.</span>
<span class="sd"> It supports both continuous and categorical features.</span>
<span class="sd"> >>> from numpy import allclose</span>
<span class="sd"> >>> from pyspark.mllib.linalg import Vectors</span>
<span class="sd"> >>> df = sqlContext.createDataFrame([</span>
<span class="sd"> ... (1.0, Vectors.dense(1.0)),</span>
<span class="sd"> ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])</span>
<span class="sd"> >>> gbt = GBTRegressor(maxIter=5, maxDepth=2)</span>
<span class="sd"> >>> model = gbt.fit(df)</span>
<span class="sd"> >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])</span>
<span class="sd"> True</span>
<span class="sd"> >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])</span>
<span class="sd"> >>> model.transform(test0).head().prediction</span>
<span class="sd"> 0.0</span>
<span class="sd"> >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])</span>
<span class="sd"> >>> model.transform(test1).head().prediction</span>
<span class="sd"> 1.0</span>
<span class="sd"> """</span>
<span class="c"># a placeholder to make it appear in the generated doc</span>
<span class="n">lossType</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">"lossType"</span><span class="p">,</span>
<span class="s">"Loss function which GBT tries to minimize (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">GBTParams</span><span class="o">.</span><span class="n">supportedLossTypes</span><span class="p">))</span>
<span class="n">subsamplingRate</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">"subsamplingRate"</span><span class="p">,</span>
<span class="s">"Fraction of the training data used for learning each decision tree, "</span> <span class="o">+</span>
<span class="s">"in range (0, 1]."</span><span class="p">)</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 (a.k.a. learning rate) in interval (0, 1] for shrinking the "</span> <span class="o">+</span>
<span class="s">"contribution of each estimator"</span><span class="p">)</span>
<span class="nd">@keyword_only</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="n">featuresCol</span><span class="o">=</span><span class="s">"features"</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="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span>
<span class="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">lossType</span><span class="o">=</span><span class="s">"squared"</span><span class="p">,</span>
<span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">stepSize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \</span>
<span class="sd"> lossType="squared", maxIter=20, stepSize=0.1)</span>
<span class="sd"> """</span>
<span class="nb">super</span><span class="p">(</span><span class="n">GBTRegressor</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="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span><span class="s">"org.apache.spark.ml.regression.GBTRegressor"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
<span class="c">#: param for Loss function which GBT tries to minimize (case-insensitive).</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lossType</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">"lossType"</span><span class="p">,</span>
<span class="s">"Loss function which GBT tries to minimize (case-insensitive). "</span> <span class="o">+</span>
<span class="s">"Supported options: "</span> <span class="o">+</span> <span class="s">", "</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">GBTParams</span><span class="o">.</span><span class="n">supportedLossTypes</span><span class="p">))</span>
<span class="c">#: Fraction of the training data used for learning each decision tree, in range (0, 1].</span>
<span class="bp">self</span><span class="o">.</span><span class="n">subsamplingRate</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">"subsamplingRate"</span><span class="p">,</span>
<span class="s">"Fraction of the training data used for learning each "</span> <span class="o">+</span>
<span class="s">"decision tree, in range (0, 1]."</span><span class="p">)</span>
<span class="c">#: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of</span>
<span class="c"># each estimator</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 (a.k.a. learning rate) in interval (0, 1] for shrinking "</span> <span class="o">+</span>
<span class="s">"the contribution of each estimator"</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">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">lossType</span><span class="o">=</span><span class="s">"squared"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">stepSize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@keyword_only</span>
<div class="viewcode-block" id="GBTRegressor.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.setParams">[docs]</a> <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</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="n">labelCol</span><span class="o">=</span><span class="s">"label"</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="n">maxDepth</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">maxBins</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">minInstancesPerNode</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">minInfoGain</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="n">maxMemoryInMB</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">cacheNodeIds</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">checkpointInterval</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">lossType</span><span class="o">=</span><span class="s">"squared"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">stepSize</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span>
<span class="sd"> maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \</span>
<span class="sd"> maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \</span>
<span class="sd"> lossType="squared", maxIter=20, stepSize=0.1)</span>
<span class="sd"> Sets params for Gradient Boosted Tree Regression.</span>
<span class="sd"> """</span>
<span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
<span class="k">def</span> <span class="nf">_create_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">java_model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">GBTRegressionModel</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
<div class="viewcode-block" id="GBTRegressor.setLossType"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.setLossType">[docs]</a> <span class="k">def</span> <span class="nf">setLossType</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:`lossType`.</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">lossType</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="GBTRegressor.getLossType"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.getLossType">[docs]</a> <span class="k">def</span> <span class="nf">getLossType</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 lossType 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">lossType</span><span class="p">)</span>
</div>
<div class="viewcode-block" id="GBTRegressor.setSubsamplingRate"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.setSubsamplingRate">[docs]</a> <span class="k">def</span> <span class="nf">setSubsamplingRate</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:`subsamplingRate`.</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">subsamplingRate</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
<span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="GBTRegressor.getSubsamplingRate"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.getSubsamplingRate">[docs]</a> <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="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>
</div>
<div class="viewcode-block" id="GBTRegressor.setStepSize"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.setStepSize">[docs]</a> <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>
</div>
<div class="viewcode-block" id="GBTRegressor.getStepSize"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressor.getStepSize">[docs]</a> <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>
</div></div>
<div class="viewcode-block" id="GBTRegressionModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.regression.GBTRegressionModel">[docs]</a><span class="k">class</span> <span class="nc">GBTRegressionModel</span><span class="p">(</span><span class="n">TreeEnsembleModels</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Model fitted by GBTRegressor.</span>
<span class="sd"> """</span>
</div>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">"__main__"</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">doctest</span>
<span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
<span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c"># The small batch size here ensures that we see multiple batches,</span>
<span class="c"># even in these small test examples:</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">"local[2]"</span><span class="p">,</span> <span class="s">"ml.regression tests"</span><span class="p">)</span>
<span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'sc'</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
<span class="n">globs</span><span class="p">[</span><span class="s">'sqlContext'</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqlContext</span>
<span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
<span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
<span class="nb">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
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