[SPARK-15134][EXAMPLE] Indent SparkSession builder patterns and update binary_classification_metrics_example.py

## What changes were proposed in this pull request?

This issue addresses the comments in SPARK-15031 and also fix java-linter errors.
- Use multiline format in SparkSession builder patterns.
- Update `binary_classification_metrics_example.py` to use `SparkSession`.
- Fix Java Linter errors (in SPARK-13745, SPARK-15031, and so far)

## How was this patch tested?

After passing the Jenkins tests and run `dev/lint-java` manually.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12911 from dongjoon-hyun/SPARK-15134.

1 files changed, 10 insertions, 5 deletions

diff --git a/examples/src/main/python/mllib/binary_classification_metrics_example.py b/examples/src/main/python/mllib/binary_classification_metrics_example.py
index 8f0fc9d45d..daf000e38d 100644
--- a/examples/src/main/python/mllib/binary_classification_metrics_example.py
+++ b/examples/src/main/python/mllib/binary_classification_metrics_example.py
@@ -18,20 +18,25 @@
 Binary Classification Metrics Example.
 """
 from __future__ import print_function
-from pyspark import SparkContext
+from pyspark.sql import SparkSession
 # $example on$
 from pyspark.mllib.classification import LogisticRegressionWithLBFGS
 from pyspark.mllib.evaluation import BinaryClassificationMetrics
-from pyspark.mllib.util import MLUtils
+from pyspark.mllib.regression import LabeledPoint
 # $example off$
 
 if __name__ == "__main__":
-    sc = SparkContext(appName="BinaryClassificationMetricsExample")
+    spark = SparkSession\
+        .builder\
+        .appName("BinaryClassificationMetricsExample")\
+        .getOrCreate()
 
     # $example on$
     # Several of the methods available in scala are currently missing from pyspark
     # Load training data in LIBSVM format
-    data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_binary_classification_data.txt")
+    data = spark\
+        .read.format("libsvm").load("data/mllib/sample_binary_classification_data.txt")\
+        .rdd.map(lambda row: LabeledPoint(row[0], row[1]))
 
     # Split data into training (60%) and test (40%)
     training, test = data.randomSplit([0.6, 0.4], seed=11L)
@@ -53,4 +58,4 @@ if __name__ == "__main__":
     print("Area under ROC = %s" % metrics.areaUnderROC)
     # $example off$
 
-    sc.stop()
+    spark.stop()