fixed scala style

liancheng · Apr 19, 2015 · e2ffae8 · e2ffae8
1 parent b8823b0
commit e2ffae8
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Showing 5 changed files with 115 additions and 86 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -35,7 +35,8 @@ import org.apache.spark.sql.Row
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
  */
-class KMeansModel (val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
+class KMeansModel (
+    val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
 
   /** A Java-friendly constructor that takes an Iterable of Vectors. */
   def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray)

diff --git a/...test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala b/...test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala
@@ -29,71 +29,85 @@ class GeneralizedLinearPMMLModelExportSuite extends FunSuite{
 
    test("GeneralizedLinearPMMLModelExport generate PMML format") {
 
-    //arrange models to test
+    // arrange models to test
     val linearInput = LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 1, 17)
-    val linearRegressionModel = new LinearRegressionModel(linearInput(0).features, linearInput(0).label);
-    val ridgeRegressionModel = new RidgeRegressionModel(linearInput(0).features, linearInput(0).label);
+    val linearRegressionModel = new LinearRegressionModel(
+        linearInput(0).features, linearInput(0).label);
+    val ridgeRegressionModel = new RidgeRegressionModel(
+        linearInput(0).features, linearInput(0).label);
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label);
     val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label);
 
-    //act by exporting the model to the PMML format
-    val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)         
-    //assert that the PMML format is as expected
+    // act by exporting the model to the PMML format
+    val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)
+    // assert that the PMML format is as expected
     assert(linearModelExport.isInstanceOf[PMMLModelExport])
     var pmml = linearModelExport.asInstanceOf[PMMLModelExport].getPmml()
     assert(pmml.getHeader().getDescription() === "linear regression")
-    //check that the number of fields match the weights size
-    assert(pmml.getDataDictionary().getNumberOfFields() === linearRegressionModel.weights.size + 1)
-    //this verify that there is a model attached to the pmml object and the model is a regression one
-    //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
+    // check that the number of fields match the weights size
+    assert(pmml.getDataDictionary().getNumberOfFields() 
+        === linearRegressionModel.weights.size + 1)
+    // this verify that there is a model attached to the pmml object 
+      // and the model is a regression one
+    // it also verifies that the pmml model has a regression table 
+      // with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
-     .getRegressionTables().get(0).getNumericPredictors().size() === linearRegressionModel.weights.size)
+     .getRegressionTables().get(0).getNumericPredictors().size()
+     === linearRegressionModel.weights.size)
 
-    //act
+    // act
     val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel)         
-    //assert that the PMML format is as expected
+    // assert that the PMML format is as expected
     assert(ridgeModelExport.isInstanceOf[PMMLModelExport])
     pmml = ridgeModelExport.asInstanceOf[PMMLModelExport].getPmml()
     assert(pmml.getHeader().getDescription() === "ridge regression")
-    //check that the number of fields match the weights size
+    // check that the number of fields match the weights size
     assert(pmml.getDataDictionary().getNumberOfFields() === ridgeRegressionModel.weights.size + 1)
-    //this verify that there is a model attached to the pmml object and the model is a regression one
-    //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
+    // this verify that there is a model attached to the pmml object 
+      // and the model is a regression one
+    // it also verifies that the pmml model has a regression table 
+      // with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
-     .getRegressionTables().get(0).getNumericPredictors().size() === ridgeRegressionModel.weights.size)
+     .getRegressionTables().get(0).getNumericPredictors().size() 
+     === ridgeRegressionModel.weights.size)
 
-    //act
+    // act
     val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel)         
-    //assert that the PMML format is as expected
+    // assert that the PMML format is as expected
     assert(lassoModelExport.isInstanceOf[PMMLModelExport])
     pmml = lassoModelExport.asInstanceOf[PMMLModelExport].getPmml()
     assert(pmml.getHeader().getDescription() === "lasso regression")
-    //check that the number of fields match the weights size
+    // check that the number of fields match the weights size
     assert(pmml.getDataDictionary().getNumberOfFields() === lassoModel.weights.size + 1)
-    //this verify that there is a model attached to the pmml object and the model is a regression one
-    //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
+    // this verify that there is a model attached to the pmml object 
+      // and the model is a regression one
+    // it also verifies that the pmml model has a regression table 
+      // with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(0).getNumericPredictors().size() === lassoModel.weights.size)
 
-    //act
+    // act
     val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)         
-    //assert that the PMML format is as expected
+    // assert that the PMML format is as expected
     assert(svmModelExport.isInstanceOf[PMMLModelExport])
     pmml = svmModelExport.asInstanceOf[PMMLModelExport].getPmml()
-    assert(pmml.getHeader().getDescription() === "linear SVM: if predicted value > 0, the outcome is positive, or negative otherwise")
-    //check that the number of fields match the weights size
+    assert(pmml.getHeader().getDescription() 
+        === "linear SVM: if predicted value > 0, the outcome is positive, or negative otherwise")
+    // check that the number of fields match the weights size
     assert(pmml.getDataDictionary().getNumberOfFields() === svmModel.weights.size + 1)
-    //this verify that there is a model attached to the pmml object and the model is a regression one
-    //it also verifies that the pmml model has a regression table with the same number of predictors of the model weights
+    // this verify that there is a model attached to the pmml object
+      // and the model is a regression one
+    // it also verifies that the pmml model has a regression table
+      // with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(0).getNumericPredictors().size() === svmModel.weights.size)
 
-    //manual checking
-    //linearRegressionModel.toPMML("/tmp/linearregression.xml")
-    //ridgeRegressionModel.toPMML("/tmp/ridgeregression.xml")
-    //lassoModel.toPMML("/tmp/lassoregression.xml")
-    //svmModel.toPMML("/tmp/linearsvm.xml")
+    // manual checking
+    // linearRegressionModel.toPMML("/tmp/linearregression.xml")
+    // ridgeRegressionModel.toPMML("/tmp/ridgeregression.xml")
+    // lassoModel.toPMML("/tmp/lassoregression.xml")
+    // svmModel.toPMML("/tmp/linearsvm.xml")
 
    }
 

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala
@@ -26,31 +26,33 @@ class KMeansPMMLModelExportSuite extends FunSuite{
 
    test("KMeansPMMLModelExport generate PMML format") {
 
-    //arrange model to test
+    // arrange model to test
     val clusterCenters = Array(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
     )
     val kmeansModel = new KMeansModel(clusterCenters);
 
-    //act by exporting the model to the PMML format
+    // act by exporting the model to the PMML format
     val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel)
 
-    //assert that the PMML format is as expected
+    // assert that the PMML format is as expected
     assert(modelExport.isInstanceOf[PMMLModelExport])
     val pmml = modelExport.asInstanceOf[PMMLModelExport].getPmml()
     assert(pmml.getHeader().getDescription() === "k-means clustering")
-    //check that the number of fields match the single vector size
+    // check that the number of fields match the single vector size
     assert(pmml.getDataDictionary().getNumberOfFields() === clusterCenters(0).size)
-    //this verify that there is a model attached to the pmml object and the model is a clustering one
-    //it also verifies that the pmml model has the same number of clusters of the spark model
-    assert(pmml.getModels().get(0).asInstanceOf[ClusteringModel].getNumberOfClusters() === clusterCenters.size)
+    // this verify that there is a model attached to the pmml object 
+      // and the model is a clustering one
+    // it also verifies that the pmml model has the same number of clusters of the spark model
+    assert(pmml.getModels().get(0).asInstanceOf[ClusteringModel].getNumberOfClusters()
+        === clusterCenters.size)
 
-    //manual checking
-    //kmeansModel.toPMML("/tmp/kmeans.xml")
-    //kmeansModel.toPMML(System.out)
-    //System.out.println(kmeansModel.toPMML())
+    // manual checking
+    // kmeansModel.toPMML("/tmp/kmeans.xml")
+    // kmeansModel.toPMML(System.out)
+    // System.out.println(kmeansModel.toPMML())
 
    }
 

diff --git a/...est/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExportSuite.scala b/...est/scala/org/apache/spark/mllib/pmml/export/LogisticRegressionPMMLModelExportSuite.scala
@@ -26,33 +26,39 @@ class LogisticRegressionPMMLModelExportSuite extends FunSuite{
 
    test("LogisticRegressionPMMLModelExport generate PMML format") {
 
-    //arrange models to test
+    // arrange models to test
     val linearInput = LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 1, 17)
-    val logisticRegressionModel = new LogisticRegressionModel(linearInput(0).features, linearInput(0).label);
+    val logisticRegressionModel = new LogisticRegressionModel(
+        linearInput(0).features, linearInput(0).label);
 
-    //act by exporting the model to the PMML format
-    val logisticModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel)         
-    //assert that the PMML format is as expected
+    // act by exporting the model to the PMML format
+    val logisticModelExport = PMMLModelExportFactory
+      .createPMMLModelExport(logisticRegressionModel)
+    // assert that the PMML format is as expected
     assert(logisticModelExport.isInstanceOf[PMMLModelExport])
     var pmml = logisticModelExport.asInstanceOf[PMMLModelExport].getPmml()
     assert(pmml.getHeader().getDescription() === "logistic regression")
-    //check that the number of fields match the weights size
-    assert(pmml.getDataDictionary().getNumberOfFields() === logisticRegressionModel.weights.size + 1)
-    //this verify that there is a model attached to the pmml object and the model is a regression one
-    //it also verifies that the pmml model has a regression table (for target category 1) with the same number of predictors of the model weights
+    // check that the number of fields match the weights size
+    assert(
+        pmml.getDataDictionary().getNumberOfFields() === logisticRegressionModel.weights.size + 1)
+    // this verify that there is a model attached to the pmml object 
+       // and the model is a regression one
+    // it also verifies that the pmml model has a regression table (for target category 1)
+       // with the same number of predictors of the model weights
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(0).getTargetCategory() === "1")
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
-     .getRegressionTables().get(0).getNumericPredictors().size() === logisticRegressionModel.weights.size)
-    //verify if there is a second table with target category 0 and no predictors
+     .getRegressionTables().get(0).getNumericPredictors().size() 
+     === logisticRegressionModel.weights.size)
+    // verify if there is a second table with target category 0 and no predictors
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(1).getTargetCategory() === "0")
     assert(pmml.getModels().get(0).asInstanceOf[RegressionModel]
      .getRegressionTables().get(1).getNumericPredictors().size() === 0)
 
-    //manual checking
-    //logisticRegressionModel.toPMML("/tmp/logisticregression.xml")
+    // manual checking
+    // logisticRegressionModel.toPMML("/tmp/logisticregression.xml")
 
    }
 

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala
@@ -31,78 +31,84 @@ class PMMLModelExportFactorySuite extends FunSuite{
 
    test("PMMLModelExportFactory create KMeansPMMLModelExport when passing a KMeansModel") {
 
-    //arrange
+    // arrange
     val clusterCenters = Array(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
     )
     val kmeansModel = new KMeansModel(clusterCenters);
 
-    //act
+    // act
     val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel)
 
-    //assert
+    // assert
     assert(modelExport.isInstanceOf[KMeansPMMLModelExport])
 
    }
 
    test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a "
-       +"LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
+       + "LinearRegressionModel, RidgeRegressionModel, LassoModel or SVMModel") {
 
-    //arrange
+    // arrange
     val linearInput = LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 1, 17)
-    val linearRegressionModel = new LinearRegressionModel(linearInput(0).features, linearInput(0).label)
-    val ridgeRegressionModel = new RidgeRegressionModel(linearInput(0).features, linearInput(0).label)
+    val linearRegressionModel = new LinearRegressionModel(
+        linearInput(0).features, linearInput(0).label)
+    val ridgeRegressionModel = new RidgeRegressionModel(
+        linearInput(0).features, linearInput(0).label)
     val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label)
     val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label)
 
-    //act
-    val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)         
-    //assert
+    // act
+    val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel)
+    // assert
     assert(linearModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
-    //act
-    val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel)         
-    //assert
+    // act
+    val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel)
+    // assert
     assert(ridgeModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
-    //act
+    // act
     val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel)         
-    //assert
+    // assert
     assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
-    //act
+    // act
     val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel)         
-    //assert
+    // assert
     assert(svmModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport])
 
    }
 
-   test("PMMLModelExportFactory create LogisticRegressionPMMLModelExport when passing a LogisticRegressionModel") {
+   test("PMMLModelExportFactory create LogisticRegressionPMMLModelExport "
+       + "when passing a LogisticRegressionModel") {
 
-    //arrange
+    // arrange
     val linearInput = LinearDataGenerator.generateLinearInput(
       3.0, Array(10.0, 10.0), 1, 17)
-    val logisticRegressionModel = new LogisticRegressionModel(linearInput(0).features, linearInput(0).label);
+    val logisticRegressionModel = new LogisticRegressionModel(
+        linearInput(0).features, linearInput(0).label);
 
-    //act
-    val logisticRegressionModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel)         
-    //assert
+    // act
+    val logisticRegressionModelExport = PMMLModelExportFactory
+      .createPMMLModelExport(logisticRegressionModel)      
+    // assert
     assert(logisticRegressionModelExport.isInstanceOf[LogisticRegressionPMMLModelExport])
 
    }
 
-   test("PMMLModelExportFactory throw IllegalArgumentException when passing an unsupported model") {
+   test("PMMLModelExportFactory throw IllegalArgumentException "
+       + "when passing an unsupported model") {
 
-    //arrange
+    // arrange
     val invalidModel = new Object;
 
-    //assert
+    // assert
     intercept[IllegalArgumentException] {
-        //act
-    	PMMLModelExportFactory.createPMMLModelExport(invalidModel)
+        // act
+      PMMLModelExportFactory.createPMMLModelExport(invalidModel)
     }
 
    }