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">"features"</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"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="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">"auto"</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">"""</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, fitIntercept=True, \</span> <span class="sd"> standardization=True, solver="auto", weightCol=None, aggregationDepth=2)</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="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">"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="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">"1.4.0"</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">"features"</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"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="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">"auto"</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">"""</span> <span class="sd"> setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</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">"No training summary available for this </span><span class="si">%s</span><span class="s2">"</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">"2.0.0"</span><span class="p">)</span> @@ -508,7 +508,7 @@ <span class="sd"> __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \</span> <span class="sd"> weightCol=None, isotonic=True, featureIndex=0):</span> <span class="sd"> """</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">"org.apache.spark.ml.regression.IsotonicRegression"</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">"1.4.0"</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">", "</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">"1.4.0"</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">"1.4.0"</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="variance", seed=None, varianceCol=None)</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="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">"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="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="variance", subsamplingRate=1.0, seed=None, numTrees=20, \</span> <span class="sd"> featureSubsetStrategy="auto")</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="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">"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="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="squared", maxIter=20, stepSize=0.1, seed=None, \</span> <span class="sd"> impurity="variance")</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="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">"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="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">"features"</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"prediction"</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">"censor"</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">"censor"</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">"""</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"> """</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">"org.apache.spark.ml.regression.AFTSurvivalRegression"</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">"censor"</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">"1.6.0"</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">"features"</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s2">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"prediction"</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">"censor"</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">"censor"</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">"""</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">"label"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s2">"features"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"prediction"</span><span class="p">,</span> - <span class="n">family</span><span class="o">=</span><span class="s2">"gaussian"</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">"gaussian"</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">"irls"</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">"""</span> <span class="sd"> __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", \</span> <span class="sd"> family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \</span> <span class="sd"> regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None)</span> <span class="sd"> """</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">"org.apache.spark.ml.regression.GeneralizedLinearRegression"</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">"gaussian"</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">"irls"</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">"gaussian"</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">"irls"</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">"2.0.0"</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">"label"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s2">"features"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s2">"prediction"</span><span class="p">,</span> - <span class="n">family</span><span class="o">=</span><span class="s2">"gaussian"</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">"gaussian"</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">"irls"</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">"""</span> <span class="sd"> setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", \</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">"No training summary available for this </span><span class="si">%s</span><span class="s2">"</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">"2.0.0"</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">"pValues"</span><span class="p">)</span></div> -<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> +<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</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"""</span> + <span class="sa">r</span><span class="sd">"""</span> <span class="sd"> Builder for a param grid used in grid search-based model selection.</span> <span class="sd"> >>> 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"> """</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"> """</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"> """</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"> """</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">"__main__"</span><span class="p">:</span> +<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__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.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">"""</span> <span class="sd"> Generate a unique unicode id for the object. The default implementation</span> <span class="sd"> concatenates the class name, "_", and 12 random hex chars.</span> <span class="sd"> """</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">"_"</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">"_"</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"> """</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">"""</span> <span class="sd"> Returns the full class name of the Java ML instance. The default</span> <span class="sd"> implementation replaces "pyspark" by "org.apache.spark" in</span> <span class="sd"> the Python full class name.</span> <span class="sd"> """</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">"pyspark"</span><span class="p">,</span> <span class="s2">"org.apache.spark"</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">"Pipeline"</span><span class="p">,</span> <span class="s2">"PipelineModel"</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">"pyspark"</span><span class="p">,</span> <span class="s2">"org.apache.spark"</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">"Pipeline"</span><span class="p">,</span> <span class="s2">"PipelineModel"</span><span class="p">):</span> <span class="c1"># Remove the last package name "pipeline" for Pipeline and PipelineModel.</span> <span class="n">java_package</span> <span class="o">=</span> <span class="s2">"."</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">java_package</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">"."</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">"."</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">"."</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">"""Load the peer Java object of the ML instance."""</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">"."</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"> """</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">"""Returns an MLReader instance for this class."""</span> - <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"MLReadable.read() not implemented for type: </span><span class="si">%r</span><span class="s2">"</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">"MLReadable.read() not implemented for type: </span><span class="si">%r</span><span class="s2">"</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">"""Reads an ML instance from the input path, a shortcut of `read().load(path)`."""</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"> """</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">"""Returns an MLReader instance for this class."""</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"> """</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">"""</span> <span class="sd"> Construct this object from given Java classname and arguments</span> <span class="sd"> """</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"> """</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"> """</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">'1.4.0'</span><span class="p">)</span> @@ -211,7 +211,7 @@ <span class="sd"> .. versionadded:: 0.9.0</span> <span class="sd"> """</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">'1.4.0'</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">"""</span> <span class="sd"> Load a model from the given path.</span> <span class="sd"> """</span> @@ -314,7 +314,7 @@ <span class="sd"> """</span> <span class="nd">@classmethod</span> <span class="nd">@since</span><span class="p">(</span><span class="s1">'0.9.0'</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="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">"l2"</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">"""</span> @@ -375,8 +375,8 @@ <span class="sd"> """</span> <span class="nd">@classmethod</span> <span class="nd">@since</span><span class="p">(</span><span class="s1">'1.2.0'</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">"l2"</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">"l2"</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">"""</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"> """</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">'0.9.0'</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">'1.4.0'</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">"""</span> <span class="sd"> Load a model from the given path.</span> <span class="sd"> """</span> @@ -550,7 +550,7 @@ <span class="nd">@classmethod</span> <span class="nd">@since</span><span class="p">(</span><span class="s1">'0.9.0'</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">"l2"</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">"""</span> @@ -680,7 +680,7 @@ <span class="nd">@classmethod</span> <span class="nd">@since</span><span class="p">(</span><span class="s1">'1.4.0'</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">"""</span> <span class="sd"> Load a model from the given path.</span> <span class="sd"> """</span> @@ -700,7 +700,7 @@ <span class="nd">@classmethod</span> <span class="nd">@since</span><span class="p">(</span><span class="s1">'0.9.0'</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="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">"""</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">'1.5.0'</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">"local[4]"</span><span class="p">)</span>\ <span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"mllib.classification tests"</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">"__main__"</span><span class="p">:</span> +<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> <span class="n">_test</span><span class="p">()</span> </pre></div> --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
