[Minor] [SQL] Cleans up DataFrame variable names and toDF() calls

Although we've migrated to the DataFrame API, lots of code still uses `rdd` or `srdd` as local variable names. This PR tries to address these naming inconsistencies and some other minor DataFrame related style issues.

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Author: Cheng Lian <[email protected]>

Closes #4670 from liancheng/df-cleanup and squashes the following commits:

3e14448 [Cheng Lian] Cleans up DataFrame variable names and toDF() calls

Author: liancheng

examples/src/main/scala/org/apache/spark/examples/ml/CrossValidatorExample.scala

@@ -90,7 +90,7 @@ object CrossValidatorExample {
     crossval.setNumFolds(2) // Use 3+ in practice
 
     // Run cross-validation, and choose the best set of parameters.
-    val cvModel = crossval.fit(training.toDF)
+    val cvModel = crossval.fit(training.toDF())
 
     // Prepare test documents, which are unlabeled.
     val test = sc.parallelize(Seq(

examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala

@@ -58,7 +58,7 @@ object DeveloperApiExample {
     lr.setMaxIter(10)
 
     // Learn a LogisticRegression model.  This uses the parameters stored in lr.
-    val model = lr.fit(training.toDF)
+    val model = lr.fit(training.toDF())
 
     // Prepare test data.
     val test = sc.parallelize(Seq(
@@ -67,7 +67,7 @@ object DeveloperApiExample {
       LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
 
     // Make predictions on test data.
-    val sumPredictions: Double = model.transform(test.toDF)
+    val sumPredictions: Double = model.transform(test.toDF())
       .select("features", "label", "prediction")
       .collect()
       .map { case Row(features: Vector, label: Double, prediction: Double) =>

examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala

@@ -137,9 +137,9 @@ object MovieLensALS {
       .setRegParam(params.regParam)
       .setNumBlocks(params.numBlocks)
 
-    val model = als.fit(training.toDF)
+    val model = als.fit(training.toDF())
 
-    val predictions = model.transform(test.toDF).cache()
+    val predictions = model.transform(test.toDF()).cache()
 
     // Evaluate the model.
     // TODO: Create an evaluator to compute RMSE.
@@ -158,7 +158,7 @@ object MovieLensALS {
 
     // Inspect false positives.
     predictions.registerTempTable("prediction")
-    sc.textFile(params.movies).map(Movie.parseMovie).toDF.registerTempTable("movie")
+    sc.textFile(params.movies).map(Movie.parseMovie).toDF().registerTempTable("movie")
     sqlContext.sql(
       """
         |SELECT userId, prediction.movieId, title, rating, prediction

examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala

@@ -58,7 +58,7 @@ object SimpleParamsExample {
       .setRegParam(0.01)
 
     // Learn a LogisticRegression model.  This uses the parameters stored in lr.
-    val model1 = lr.fit(training.toDF)
+    val model1 = lr.fit(training.toDF())
     // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
     // we can view the parameters it used during fit().
     // This prints the parameter (name: value) pairs, where names are unique IDs for this
@@ -77,7 +77,7 @@ object SimpleParamsExample {
 
     // Now learn a new model using the paramMapCombined parameters.
     // paramMapCombined overrides all parameters set earlier via lr.set* methods.
-    val model2 = lr.fit(training.toDF, paramMapCombined)
+    val model2 = lr.fit(training.toDF(), paramMapCombined)
     println("Model 2 was fit using parameters: " + model2.fittingParamMap)
 
     // Prepare test data.
@@ -90,7 +90,7 @@ object SimpleParamsExample {
     // LogisticRegression.transform will only use the 'features' column.
     // Note that model2.transform() outputs a 'myProbability' column instead of the usual
     // 'probability' column since we renamed the lr.probabilityCol parameter previously.
-    model2.transform(test.toDF)
+    model2.transform(test.toDF())
       .select("features", "label", "myProbability", "prediction")
       .collect()
       .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>

examples/src/main/scala/org/apache/spark/examples/ml/SimpleTextClassificationPipeline.scala

@@ -69,7 +69,7 @@ object SimpleTextClassificationPipeline {
       .setStages(Array(tokenizer, hashingTF, lr))
 
     // Fit the pipeline to training documents.
-    val model = pipeline.fit(training.toDF)
+    val model = pipeline.fit(training.toDF())
 
     // Prepare test documents, which are unlabeled.
     val test = sc.parallelize(Seq(
@@ -79,7 +79,7 @@ object SimpleTextClassificationPipeline {
       Document(7L, "apache hadoop")))
 
     // Make predictions on test documents.
-    model.transform(test.toDF)
+    model.transform(test.toDF())
       .select("id", "text", "probability", "prediction")
       .collect()
       .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>

examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala

@@ -81,7 +81,7 @@ object DatasetExample {
     println(s"Loaded ${origData.count()} instances from file: ${params.input}")
 
     // Convert input data to DataFrame explicitly.
-    val df: DataFrame = origData.toDF
+    val df: DataFrame = origData.toDF()
     println(s"Inferred schema:\n${df.schema.prettyJson}")
     println(s"Converted to DataFrame with ${df.count()} records")
 

examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala

@@ -34,7 +34,7 @@ object RDDRelation {
     // Importing the SQL context gives access to all the SQL functions and implicit conversions.
     import sqlContext.implicits._
 
-    val df = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))).toDF
+    val df = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i"))).toDF()
     // Any RDD containing case classes can be registered as a table.  The schema of the table is
     // automatically inferred using scala reflection.
     df.registerTempTable("records")

examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala

@@ -68,7 +68,7 @@ object HiveFromSpark {
 
     // You can also register RDDs as temporary tables within a HiveContext.
     val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
-    rdd.toDF.registerTempTable("records")
+    rdd.toDF().registerTempTable("records")
 
     // Queries can then join RDD data with data stored in Hive.
     println("Result of SELECT *:")

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -102,7 +102,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
       sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
 
       // Create Parquet data.
-      val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF
+      val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
       dataRDD.saveAsParquetFile(dataPath(path))
     }
 

mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala

@@ -62,7 +62,7 @@ private[classification] object GLMClassificationModel {
 
       // Create Parquet data.
       val data = Data(weights, intercept, threshold)
-      sc.parallelize(Seq(data), 1).toDF.saveAsParquetFile(Loader.dataPath(path))
+      sc.parallelize(Seq(data), 1).toDF().saveAsParquetFile(Loader.dataPath(path))
     }
 
     /**