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Posted to commits@spark.apache.org by gu...@apache.org on 2018/07/03 18:10:29 UTC

[11/14] spark-website git commit: Fix signature description broken in PySpark API documentation in 2.1.2

http://git-wip-us.apache.org/repos/asf/spark-website/blob/6bbac496/site/docs/2.1.2/api/python/_modules/pyspark/ml/regression.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.2/api/python/_modules/pyspark/ml/regression.html b/site/docs/2.1.2/api/python/_modules/pyspark/ml/regression.html
index f3d5915..4f73b9e 100644
--- a/site/docs/2.1.2/api/python/_modules/pyspark/ml/regression.html
+++ b/site/docs/2.1.2/api/python/_modules/pyspark/ml/regression.html
@@ -145,24 +145,24 @@
 
     <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="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</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="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                  <span class="n">standardization</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="n">weightCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aggregationDepth</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        __init__(self, featuresCol=&quot;features&quot;, labelCol=&quot;label&quot;, predictionCol=&quot;prediction&quot;, \</span>
 <span class="sd">                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \</span>
 <span class="sd">                 standardization=True, solver=&quot;auto&quot;, weightCol=None, aggregationDepth=2)</span>
 <span class="sd">        &quot;&quot;&quot;</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="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="fm">__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="s2">&quot;org.apache.spark.ml.regression.LinearRegression&quot;</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="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">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</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">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">_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>
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
 <div class="viewcode-block" id="LinearRegression.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.classification.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="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</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="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                   <span class="n">standardization</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="n">weightCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aggregationDepth</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        setParams(self, featuresCol=&quot;features&quot;, labelCol=&quot;label&quot;, predictionCol=&quot;prediction&quot;, \</span>
@@ -213,7 +213,7 @@
             <span class="k">return</span> <span class="n">LinearRegressionTrainingSummary</span><span class="p">(</span><span class="n">java_lrt_summary</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;No training summary available for this </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
-                               <span class="bp">self</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span><span class="p">)</span>
+                               <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
 
     <span class="nd">@property</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
@@ -508,7 +508,7 @@
 <span class="sd">        __init__(self, featuresCol=&quot;features&quot;, labelCol=&quot;label&quot;, predictionCol=&quot;prediction&quot;, \</span>
 <span class="sd">                 weightCol=None, isotonic=True, featureIndex=0):</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">IsotonicRegression</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="nb">super</span><span class="p">(</span><span class="n">IsotonicRegression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="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="s2">&quot;org.apache.spark.ml.regression.IsotonicRegression&quot;</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="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">isotonic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">featureIndex</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -589,7 +589,7 @@
                             <span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toFloat</span><span class="p">)</span>
 
     <span class="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">TreeEnsembleParams</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="nb">super</span><span class="p">(</span><span class="n">TreeEnsembleParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">setSubsamplingRate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
@@ -618,7 +618,7 @@
                      <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">supportedImpurities</span><span class="p">),</span> <span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">)</span>
 
     <span class="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">TreeRegressorParams</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="nb">super</span><span class="p">(</span><span class="n">TreeRegressorParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">setImpurity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
@@ -650,7 +650,7 @@
               <span class="n">typeConverter</span><span class="o">=</span><span class="n">TypeConverters</span><span class="o">.</span><span class="n">toString</span><span class="p">)</span>
 
     <span class="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">RandomForestParams</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="nb">super</span><span class="p">(</span><span class="n">RandomForestParams</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">)</span>
     <span class="k">def</span> <span class="nf">setNumTrees</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
@@ -746,7 +746,7 @@
 <span class="sd">                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \</span>
 <span class="sd">                 impurity=&quot;variance&quot;, seed=None, varianceCol=None)</span>
 <span class="sd">        &quot;&quot;&quot;</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="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="fm">__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="s2">&quot;org.apache.spark.ml.regression.DecisionTreeRegressor&quot;</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="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>
@@ -936,7 +936,7 @@
 <span class="sd">                 impurity=&quot;variance&quot;, subsamplingRate=1.0, seed=None, numTrees=20, \</span>
 <span class="sd">                 featureSubsetStrategy=&quot;auto&quot;)</span>
 <span class="sd">        &quot;&quot;&quot;</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="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="fm">__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="s2">&quot;org.apache.spark.ml.regression.RandomForestRegressor&quot;</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="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>
@@ -1064,7 +1064,7 @@
 <span class="sd">                 checkpointInterval=10, lossType=&quot;squared&quot;, maxIter=20, stepSize=0.1, seed=None, \</span>
 <span class="sd">                 impurity=&quot;variance&quot;)</span>
 <span class="sd">        &quot;&quot;&quot;</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="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="fm">__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="s2">&quot;org.apache.spark.ml.regression.GBTRegressor&quot;</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="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="kc">False</span><span class="p">,</span> <span class="n">subsamplingRate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
@@ -1204,7 +1204,7 @@
 
     <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="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</span><span class="p">,</span>
-                 <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">E</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">censorCol</span><span class="o">=</span><span class="s2">&quot;censor&quot;</span><span class="p">,</span>
+                 <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">tol</span><span class="o">=</span><span class="mf">1E-6</span><span class="p">,</span> <span class="n">censorCol</span><span class="o">=</span><span class="s2">&quot;censor&quot;</span><span class="p">,</span>
                  <span class="n">quantileProbabilities</span><span class="o">=</span><span class="nb">list</span><span class="p">([</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">]),</span>
                  <span class="n">quantilesCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aggregationDepth</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
@@ -1213,19 +1213,19 @@
 <span class="sd">                 quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \</span>
 <span class="sd">                 quantilesCol=None, aggregationDepth=2)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">AFTSurvivalRegression</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="nb">super</span><span class="p">(</span><span class="n">AFTSurvivalRegression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="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="s2">&quot;org.apache.spark.ml.regression.AFTSurvivalRegression&quot;</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="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">censorCol</span><span class="o">=</span><span class="s2">&quot;censor&quot;</span><span class="p">,</span>
                          <span class="n">quantileProbabilities</span><span class="o">=</span><span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.99</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">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">E</span><span class="o">-</span><span class="mi">6</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">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">_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>
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;1.6.0&quot;</span><span class="p">)</span>
 <div class="viewcode-block" id="AFTSurvivalRegression.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.classification.AFTSurvivalRegression.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="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</span><span class="p">,</span>
-                  <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">E</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">censorCol</span><span class="o">=</span><span class="s2">&quot;censor&quot;</span><span class="p">,</span>
+                  <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</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">tol</span><span class="o">=</span><span class="mf">1E-6</span><span class="p">,</span> <span class="n">censorCol</span><span class="o">=</span><span class="s2">&quot;censor&quot;</span><span class="p">,</span>
                   <span class="n">quantileProbabilities</span><span class="o">=</span><span class="nb">list</span><span class="p">([</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">]),</span>
                   <span class="n">quantilesCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">aggregationDepth</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
@@ -1403,24 +1403,24 @@
 
     <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">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</span><span class="p">,</span>
-                 <span class="n">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span>
+                 <span class="n">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</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">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">weightCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;irls&quot;</span><span class="p">,</span> <span class="n">linkPredictionCol</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        __init__(self, labelCol=&quot;label&quot;, featuresCol=&quot;features&quot;, predictionCol=&quot;prediction&quot;, \</span>
 <span class="sd">                 family=&quot;gaussian&quot;, link=None, fitIntercept=True, maxIter=25, tol=1e-6, \</span>
 <span class="sd">                 regParam=0.0, weightCol=None, solver=&quot;irls&quot;, linkPredictionCol=None)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">GeneralizedLinearRegression</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="nb">super</span><span class="p">(</span><span class="n">GeneralizedLinearRegression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="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="s2">&quot;org.apache.spark.ml.regression.GeneralizedLinearRegression&quot;</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="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</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">solver</span><span class="o">=</span><span class="s2">&quot;irls&quot;</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">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</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">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;irls&quot;</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">_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>
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
 <div class="viewcode-block" id="GeneralizedLinearRegression.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.classification.GeneralizedLinearRegression.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">labelCol</span><span class="o">=</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s2">&quot;features&quot;</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">&quot;prediction&quot;</span><span class="p">,</span>
-                  <span class="n">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span>
+                  <span class="n">family</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span> <span class="n">link</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fitIntercept</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">25</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">regParam</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">weightCol</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s2">&quot;irls&quot;</span><span class="p">,</span> <span class="n">linkPredictionCol</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        setParams(self, labelCol=&quot;label&quot;, featuresCol=&quot;features&quot;, predictionCol=&quot;prediction&quot;, \</span>
@@ -1516,7 +1516,7 @@
             <span class="k">return</span> <span class="n">GeneralizedLinearRegressionTrainingSummary</span><span class="p">(</span><span class="n">java_glrt_summary</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;No training summary available for this </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span>
-                               <span class="bp">self</span><span class="o">.</span><span class="n">__class__</span><span class="o">.</span><span class="n">__name__</span><span class="p">)</span>
+                               <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
 
     <span class="nd">@property</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.0.0&quot;</span><span class="p">)</span>
@@ -1706,11 +1706,11 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_java</span><span class="p">(</span><span class="s2">&quot;pValues&quot;</span><span class="p">)</span></div>
 
 
-<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">doctest</span>
     <span class="kn">import</span> <span class="nn">pyspark.ml.regression</span>
     <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="k">import</span> <span class="n">SparkSession</span>
-    <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">ml</span><span class="o">.</span><span class="n">regression</span><span class="o">.</span><span class="n">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">ml</span><span class="o">.</span><span class="n">regression</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="c1"># The small batch size here ensures that we see multiple batches,</span>
     <span class="c1"># even in these small test examples:</span>
     <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span>\

http://git-wip-us.apache.org/repos/asf/spark-website/blob/6bbac496/site/docs/2.1.2/api/python/_modules/pyspark/ml/tuning.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.2/api/python/_modules/pyspark/ml/tuning.html b/site/docs/2.1.2/api/python/_modules/pyspark/ml/tuning.html
index b9d5c1f..c152787 100644
--- a/site/docs/2.1.2/api/python/_modules/pyspark/ml/tuning.html
+++ b/site/docs/2.1.2/api/python/_modules/pyspark/ml/tuning.html
@@ -80,7 +80,7 @@
 
 
 <div class="viewcode-block" id="ParamGridBuilder"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.tuning.ParamGridBuilder">[docs]</a><span class="k">class</span> <span class="nc">ParamGridBuilder</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">r&quot;&quot;&quot;</span>
+    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Builder for a param grid used in grid search-based model selection.</span>
 
 <span class="sd">    &gt;&gt;&gt; from pyspark.ml.classification import LogisticRegression</span>
@@ -232,7 +232,7 @@
 <span class="sd">        __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\</span>
 <span class="sd">                 seed=None)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">CrossValidator</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="nb">super</span><span class="p">(</span><span class="n">CrossValidator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">numFolds</span><span class="o">=</span><span class="mi">3</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">_input_kwargs</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>
@@ -325,7 +325,7 @@
 <span class="sd">    &quot;&quot;&quot;</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">bestModel</span><span class="p">,</span> <span class="n">avgMetrics</span><span class="o">=</span><span class="p">[]):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">CrossValidatorModel</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="nb">super</span><span class="p">(</span><span class="n">CrossValidatorModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1">#: best model from cross validation</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bestModel</span> <span class="o">=</span> <span class="n">bestModel</span>
         <span class="c1">#: Average cross-validation metrics for each paramMap in</span>
@@ -392,7 +392,7 @@
 <span class="sd">        __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\</span>
 <span class="sd">                 seed=None)</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">TrainValidationSplit</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="nb">super</span><span class="p">(</span><span class="n">TrainValidationSplit</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">trainRatio</span><span class="o">=</span><span class="mf">0.75</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">_input_kwargs</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>
@@ -478,7 +478,7 @@
 <span class="sd">    &quot;&quot;&quot;</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">bestModel</span><span class="p">,</span> <span class="n">validationMetrics</span><span class="o">=</span><span class="p">[]):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">TrainValidationSplitModel</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="nb">super</span><span class="p">(</span><span class="n">TrainValidationSplitModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1">#: best model from cross validation</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bestModel</span> <span class="o">=</span> <span class="n">bestModel</span>
         <span class="c1">#: evaluated validation metrics</span>
@@ -506,7 +506,7 @@
         <span class="k">return</span> <span class="n">TrainValidationSplitModel</span><span class="p">(</span><span class="n">bestModel</span><span class="p">,</span> <span class="n">validationMetrics</span><span class="p">)</span></div></div>
 
 
-<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">doctest</span>
 
     <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="k">import</span> <span class="n">SparkSession</span>

http://git-wip-us.apache.org/repos/asf/spark-website/blob/6bbac496/site/docs/2.1.2/api/python/_modules/pyspark/ml/util.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.2/api/python/_modules/pyspark/ml/util.html b/site/docs/2.1.2/api/python/_modules/pyspark/ml/util.html
index d07cd84..cda1458 100644
--- a/site/docs/2.1.2/api/python/_modules/pyspark/ml/util.html
+++ b/site/docs/2.1.2/api/python/_modules/pyspark/ml/util.html
@@ -100,12 +100,12 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">_randomUID</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_randomUID</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generate a unique unicode id for the object. The default implementation</span>
 <span class="sd">        concatenates the class name, &quot;_&quot;, and 12 random hex chars.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">unicode</span><span class="p">(</span><span class="n">cls</span><span class="o">.</span><span class="n">__name__</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">uuid</span><span class="o">.</span><span class="n">uuid4</span><span class="p">()</span><span class="o">.</span><span class="n">hex</span><span class="p">[</span><span class="mi">12</span><span class="p">:])</span>
+        <span class="k">return</span> <span class="n">unicode</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">uuid</span><span class="o">.</span><span class="n">uuid4</span><span class="p">()</span><span class="o">.</span><span class="n">hex</span><span class="p">[</span><span class="mi">12</span><span class="p">:])</span>
 
 
 <span class="nd">@inherit_doc</span>
@@ -143,7 +143,7 @@
 <span class="sd">    &quot;&quot;&quot;</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">instance</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">JavaMLWriter</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="nb">super</span><span class="p">(</span><span class="n">JavaMLWriter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="n">_java_obj</span> <span class="o">=</span> <span class="n">instance</span><span class="o">.</span><span class="n">_to_java</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_jwrite</span> <span class="o">=</span> <span class="n">_java_obj</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
 
@@ -260,22 +260,22 @@
         <span class="k">return</span> <span class="bp">self</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">clazz</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_java_loader_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">clazz</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the full class name of the Java ML instance. The default</span>
 <span class="sd">        implementation replaces &quot;pyspark&quot; by &quot;org.apache.spark&quot; in</span>
 <span class="sd">        the Python full class name.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">java_package</span> <span class="o">=</span> <span class="n">clazz</span><span class="o">.</span><span class="n">__module__</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;pyspark&quot;</span><span class="p">,</span> <span class="s2">&quot;org.apache.spark&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">clazz</span><span class="o">.</span><span class="n">__name__</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;Pipeline&quot;</span><span class="p">,</span> <span class="s2">&quot;PipelineModel&quot;</span><span class="p">):</span>
+        <span class="n">java_package</span> <span class="o">=</span> <span class="n">clazz</span><span class="o">.</span><span class="vm">__module__</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;pyspark&quot;</span><span class="p">,</span> <span class="s2">&quot;org.apache.spark&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">clazz</span><span class="o">.</span><span class="vm">__name__</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;Pipeline&quot;</span><span class="p">,</span> <span class="s2">&quot;PipelineModel&quot;</span><span class="p">):</span>
             <span class="c1"># Remove the last package name &quot;pipeline&quot; for Pipeline and PipelineModel.</span>
             <span class="n">java_package</span> <span class="o">=</span> <span class="s2">&quot;.&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">java_package</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">java_package</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span> <span class="o">+</span> <span class="n">clazz</span><span class="o">.</span><span class="n">__name__</span>
+        <span class="k">return</span> <span class="n">java_package</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span> <span class="o">+</span> <span class="n">clazz</span><span class="o">.</span><span class="vm">__name__</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">_load_java_obj</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">clazz</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_load_java_obj</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">clazz</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Load the peer Java object of the ML instance.&quot;&quot;&quot;</span>
-        <span class="n">java_class</span> <span class="o">=</span> <span class="n">cls</span><span class="o">.</span><span class="n">_java_loader_class</span><span class="p">(</span><span class="n">clazz</span><span class="p">)</span>
+        <span class="n">java_class</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_java_loader_class</span><span class="p">(</span><span class="n">clazz</span><span class="p">)</span>
         <span class="n">java_obj</span> <span class="o">=</span> <span class="n">_jvm</span><span class="p">()</span>
         <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">java_class</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">):</span>
             <span class="n">java_obj</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">java_obj</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
@@ -291,14 +291,14 @@
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Returns an MLReader instance for this class.&quot;&quot;&quot;</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;MLReadable.read() not implemented for type: </span><span class="si">%r</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">cls</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;MLReadable.read() not implemented for type: </span><span class="si">%r</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="bp">cls</span><span class="p">)</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Reads an ML instance from the input path, a shortcut of `read().load(path)`.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">cls</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">read</span><span class="p">()</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
 
 
 <span class="nd">@inherit_doc</span>
@@ -308,9 +308,9 @@
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">cls</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Returns an MLReader instance for this class.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">JavaMLReader</span><span class="p">(</span><span class="n">cls</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">JavaMLReader</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
 
 
 <span class="nd">@inherit_doc</span>

http://git-wip-us.apache.org/repos/asf/spark-website/blob/6bbac496/site/docs/2.1.2/api/python/_modules/pyspark/ml/wrapper.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.2/api/python/_modules/pyspark/ml/wrapper.html b/site/docs/2.1.2/api/python/_modules/pyspark/ml/wrapper.html
index 036d53b..e1abca7 100644
--- a/site/docs/2.1.2/api/python/_modules/pyspark/ml/wrapper.html
+++ b/site/docs/2.1.2/api/python/_modules/pyspark/ml/wrapper.html
@@ -78,16 +78,16 @@
 <span class="sd">    Wrapper class for a Java companion object</span>
 <span class="sd">    &quot;&quot;&quot;</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">java_obj</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">JavaWrapper</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="nb">super</span><span class="p">(</span><span class="n">JavaWrapper</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_java_obj</span> <span class="o">=</span> <span class="n">java_obj</span>
 
     <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">_create_from_java_class</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">java_class</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_create_from_java_class</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">java_class</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct this object from given Java classname and arguments</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">java_obj</span> <span class="o">=</span> <span class="n">JavaWrapper</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span><span class="n">java_class</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">cls</span><span class="p">(</span><span class="n">java_obj</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">java_obj</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">_call_java</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
         <span class="n">m</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_java_obj</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
@@ -324,7 +324,7 @@
 <span class="sd">        these wrappers depend on pyspark.ml.util (both directly and via</span>
 <span class="sd">        other ML classes).</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">JavaModel</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="n">java_model</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">JavaModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">java_model</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">java_model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_resetUid</span><span class="p">(</span><span class="n">java_model</span><span class="o">.</span><span class="n">uid</span><span class="p">())</span>
 </pre></div>

http://git-wip-us.apache.org/repos/asf/spark-website/blob/6bbac496/site/docs/2.1.2/api/python/_modules/pyspark/mllib/classification.html
----------------------------------------------------------------------
diff --git a/site/docs/2.1.2/api/python/_modules/pyspark/mllib/classification.html b/site/docs/2.1.2/api/python/_modules/pyspark/mllib/classification.html
index 9c732df..77aebbd 100644
--- a/site/docs/2.1.2/api/python/_modules/pyspark/mllib/classification.html
+++ b/site/docs/2.1.2/api/python/_modules/pyspark/mllib/classification.html
@@ -90,7 +90,7 @@
 <span class="sd">    model. The categories are represented by int values: 0, 1, 2, etc.</span>
 <span class="sd">    &quot;&quot;&quot;</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">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">LinearClassificationModel</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="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">LinearClassificationModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span> <span class="o">=</span> <span class="kc">None</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.4.0&#39;</span><span class="p">)</span>
@@ -211,7 +211,7 @@
 <span class="sd">    .. versionadded:: 0.9.0</span>
 <span class="sd">    &quot;&quot;&quot;</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">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">,</span> <span class="n">numFeatures</span><span class="p">,</span> <span class="n">numClasses</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">LogisticRegressionModel</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="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">LogisticRegressionModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_numFeatures</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numFeatures</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_numClasses</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">numClasses</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span> <span class="o">=</span> <span class="mf">0.5</span>
@@ -290,7 +290,7 @@
 
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.4.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="LogisticRegressionModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+<div class="viewcode-block" id="LogisticRegressionModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Load a model from the given path.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -314,7 +314,7 @@
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;0.9.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="LogisticRegressionWithSGD.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithSGD.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">miniBatchFraction</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+<div class="viewcode-block" id="LogisticRegressionWithSGD.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithSGD.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">miniBatchFraction</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
               <span class="n">initialWeights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">regType</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">,</span> <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
               <span class="n">validateData</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">convergenceTol</span><span class="o">=</span><span class="mf">0.001</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
@@ -375,8 +375,8 @@
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.2.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="LogisticRegressionWithLBFGS.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">initialWeights</span><span class="o">=</span><span class="kc">None</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">regType</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">,</span>
-              <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">corrections</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mi">1</span><span class="n">e</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span> <span class="n">validateData</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">numClasses</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<div class="viewcode-block" id="LogisticRegressionWithLBFGS.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">initialWeights</span><span class="o">=</span><span class="kc">None</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">regType</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">,</span>
+              <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">corrections</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">validateData</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">numClasses</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Train a logistic regression model on the given data.</span>
 
@@ -499,7 +499,7 @@
 <span class="sd">    .. versionadded:: 0.9.0</span>
 <span class="sd">    &quot;&quot;&quot;</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">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">SVMModel</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="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SVMModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">weights</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_threshold</span> <span class="o">=</span> <span class="mf">0.0</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;0.9.0&#39;</span><span class="p">)</span>
@@ -529,7 +529,7 @@
 
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.4.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="SVMModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.SVMModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+<div class="viewcode-block" id="SVMModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.SVMModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Load a model from the given path.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -550,7 +550,7 @@
 
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;0.9.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="SVMWithSGD.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.SVMWithSGD.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
+<div class="viewcode-block" id="SVMWithSGD.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.SVMWithSGD.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
               <span class="n">miniBatchFraction</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">initialWeights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">regType</span><span class="o">=</span><span class="s2">&quot;l2&quot;</span><span class="p">,</span>
               <span class="n">intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">validateData</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">convergenceTol</span><span class="o">=</span><span class="mf">0.001</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
@@ -680,7 +680,7 @@
 
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.4.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="NaiveBayesModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+<div class="viewcode-block" id="NaiveBayesModel.load"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel.load">[docs]</a>    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Load a model from the given path.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -700,7 +700,7 @@
 
     <span class="nd">@classmethod</span>
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;0.9.0&#39;</span><span class="p">)</span>
-<div class="viewcode-block" id="NaiveBayes.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<div class="viewcode-block" id="NaiveBayes.train"><a class="viewcode-back" href="../../../pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Train a Naive Bayes model given an RDD of (label, features)</span>
 <span class="sd">        vectors.</span>
@@ -763,7 +763,7 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">miniBatchFraction</span> <span class="o">=</span> <span class="n">miniBatchFraction</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">convergenceTol</span> <span class="o">=</span> <span class="n">convergenceTol</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_model</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">StreamingLogisticRegressionWithSGD</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="nb">super</span><span class="p">(</span><span class="n">StreamingLogisticRegressionWithSGD</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
             <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_model</span><span class="p">)</span>
 
     <span class="nd">@since</span><span class="p">(</span><span class="s1">&#39;1.5.0&#39;</span><span class="p">)</span>
@@ -800,7 +800,7 @@
     <span class="kn">import</span> <span class="nn">doctest</span>
     <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="k">import</span> <span class="n">SparkSession</span>
     <span class="kn">import</span> <span class="nn">pyspark.mllib.classification</span>
-    <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">mllib</span><span class="o">.</span><span class="n">classification</span><span class="o">.</span><span class="n">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">mllib</span><span class="o">.</span><span class="n">classification</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span>\
         <span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">&quot;local[4]&quot;</span><span class="p">)</span>\
         <span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">&quot;mllib.classification tests&quot;</span><span class="p">)</span>\
@@ -811,7 +811,7 @@
     <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
         <span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
-<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="n">_test</span><span class="p">()</span>
 </pre></div>
 


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