[SPARK-11978][ML] Move dataset_example.py to examples/ml and rename to dataframe_example.py

Since ```Dataset``` has a new meaning in Spark 1.6, we should rename it to avoid confusion.
#9873 finished the work of Scala example, here we focus on the Python one.
Move dataset_example.py to ```examples/ml``` and rename to ```dataframe_example.py```.
BTW, fix minor missing issues of #9873.
cc mengxr

Author: Yanbo Liang <[email protected]>

Closes #9957 from yanboliang/SPARK-11978.

Author: yanboliang

examples/src/main/python/mllib/dataset_example.py renamed to examples/src/main/python/ml/dataframe_example.py

@@ -16,8 +16,8 @@
 #
 
 """
-An example of how to use DataFrame as a dataset for ML. Run with::
-    bin/spark-submit examples/src/main/python/mllib/dataset_example.py
+An example of how to use DataFrame for ML. Run with::
+    bin/spark-submit examples/src/main/python/ml/dataframe_example.py <input>
 """
 from __future__ import print_function
 
@@ -28,36 +28,48 @@
 
 from pyspark import SparkContext
 from pyspark.sql import SQLContext
-from pyspark.mllib.util import MLUtils
 from pyspark.mllib.stat import Statistics
 
-
-def summarize(dataset):
-    print("schema: %s" % dataset.schema().json())
-    labels = dataset.map(lambda r: r.label)
-    print("label average: %f" % labels.mean())
-    features = dataset.map(lambda r: r.features)
-    summary = Statistics.colStats(features)
-    print("features average: %r" % summary.mean())
-
 if __name__ == "__main__":
     if len(sys.argv) > 2:
-        print("Usage: dataset_example.py <libsvm file>", file=sys.stderr)
+        print("Usage: dataframe_example.py <libsvm file>", file=sys.stderr)
         exit(-1)
-    sc = SparkContext(appName="DatasetExample")
+    sc = SparkContext(appName="DataFrameExample")
     sqlContext = SQLContext(sc)
     if len(sys.argv) == 2:
         input = sys.argv[1]
     else:
         input = "data/mllib/sample_libsvm_data.txt"
-    points = MLUtils.loadLibSVMFile(sc, input)
-    dataset0 = sqlContext.inferSchema(points).setName("dataset0").cache()
-    summarize(dataset0)
+
+    # Load input data
+    print("Loading LIBSVM file with UDT from " + input + ".")
+    df = sqlContext.read.format("libsvm").load(input).cache()
+    print("Schema from LIBSVM:")
+    df.printSchema()
+    print("Loaded training data as a DataFrame with " +
+          str(df.count()) + " records.")
+
+    # Show statistical summary of labels.
+    labelSummary = df.describe("label")
+    labelSummary.show()
+
+    # Convert features column to an RDD of vectors.
+    features = df.select("features").map(lambda r: r.features)
+    summary = Statistics.colStats(features)
+    print("Selected features column with average values:\n" +
+          str(summary.mean()))
+
+    # Save the records in a parquet file.
     tempdir = tempfile.NamedTemporaryFile(delete=False).name
     os.unlink(tempdir)
-    print("Save dataset as a Parquet file to %s." % tempdir)
-    dataset0.saveAsParquetFile(tempdir)
-    print("Load it back and summarize it again.")
-    dataset1 = sqlContext.parquetFile(tempdir).setName("dataset1").cache()
-    summarize(dataset1)
+    print("Saving to " + tempdir + " as Parquet file.")
+    df.write.parquet(tempdir)
+
+    # Load the records back.
+    print("Loading Parquet file with UDT from " + tempdir)
+    newDF = sqlContext.read.parquet(tempdir)
+    print("Schema from Parquet:")
+    newDF.printSchema()
     shutil.rmtree(tempdir)
+
+    sc.stop()

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

@@ -44,10 +44,10 @@ object DataFrameExample {
   def main(args: Array[String]) {
     val defaultParams = Params()
 
-    val parser = new OptionParser[Params]("DatasetExample") {
-      head("Dataset: an example app using DataFrame as a Dataset for ML.")
+    val parser = new OptionParser[Params]("DataFrameExample") {
+      head("DataFrameExample: an example app using DataFrame for ML.")
       opt[String]("input")
-        .text(s"input path to dataset")
+        .text(s"input path to dataframe")
         .action((x, c) => c.copy(input = x))
       checkConfig { params =>
         success
@@ -88,7 +88,7 @@ object DataFrameExample {
     // Save the records in a parquet file.
     val tmpDir = Files.createTempDir()
     tmpDir.deleteOnExit()
-    val outputDir = new File(tmpDir, "dataset").toString
+    val outputDir = new File(tmpDir, "dataframe").toString
     println(s"Saving to $outputDir as Parquet file.")
     df.write.parquet(outputDir)