Merge pull request #414 from soulmachine/code-style

Code clean up for mllib

* Removed unnecessary parentheses
* Removed unused imports
* Simplified `filter...size()` to `count ...`
* Removed obsoleted parameters' comments

7 files changed, 11 insertions, 22 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 34c67294e9..02ede71137 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -80,9 +80,9 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with Shoul
   }
 
   def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
-    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
-      (prediction != expected.label)
-    }.size
+    val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
+      prediction != expected.label
+    }
     // At least 83% of the predictions should be on.
     ((input.length - numOffPredictions).toDouble / input.length) should be > 0.83
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index 6a957e3ddc..3357b86f9b 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.mllib.classification
 
 import scala.util.Random
-import scala.math.signum
 import scala.collection.JavaConversions._
 
 import org.scalatest.BeforeAndAfterAll
@@ -50,7 +49,7 @@ object SVMSuite {
     val x = Array.fill[Array[Double]](nPoints)(
         Array.fill[Double](weights.length)(rnd.nextDouble() * 2.0 - 1.0))
     val y = x.map { xi =>
-      val yD = (new DoubleMatrix(1, xi.length, xi:_*)).dot(weightsMat) +
+      val yD = new DoubleMatrix(1, xi.length, xi: _*).dot(weightsMat) +
         intercept + 0.01 * rnd.nextGaussian()
       if (yD < 0) 0.0 else 1.0
     }
@@ -72,9 +71,9 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
   }
 
   def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
-    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
-      (prediction != expected.label)
-    }.size
+    val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
+      prediction != expected.label
+    }
     // At least 80% of the predictions should be on.
     assert(numOffPredictions < input.length / 5)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
index 94245f6027..73657cac89 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala
@@ -17,15 +17,12 @@
 
 package org.apache.spark.mllib.clustering
 
-import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
 
-import org.jblas._
 
 class KMeansSuite extends FunSuite with BeforeAndAfterAll {
   @transient private var sc: SparkContext = _
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index e683a90f57..4e8dbde658 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -24,7 +24,6 @@ import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
 
 import org.jblas._
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
index db980c7bae..b2c8df97a8 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LassoSuite.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.mllib.regression
 
-import scala.collection.JavaConversions._
-import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
@@ -41,10 +39,10 @@ class LassoSuite extends FunSuite with BeforeAndAfterAll {
   }
 
   def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
-    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
+    val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
       // A prediction is off if the prediction is more than 0.5 away from expected value.
       math.abs(prediction - expected.label) > 0.5
-    }.size
+    }
     // At least 80% of the predictions should be on.
     assert(numOffPredictions < input.length / 5)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
index ef500c704c..406afbaa3e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LinearRegressionSuite.scala
@@ -21,7 +21,6 @@ import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.util.LinearDataGenerator
 
 class LinearRegressionSuite extends FunSuite with BeforeAndAfterAll {
@@ -37,10 +36,10 @@ class LinearRegressionSuite extends FunSuite with BeforeAndAfterAll {
   }
 
   def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
-    val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
+    val numOffPredictions = predictions.zip(input).count { case (prediction, expected) =>
       // A prediction is off if the prediction is more than 0.5 away from expected value.
       math.abs(prediction - expected.label) > 0.5
-    }.size
+    }
     // At least 80% of the predictions should be on.
     assert(numOffPredictions < input.length / 5)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
index c18092d804..1d6a10b66e 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala
@@ -17,15 +17,12 @@
 
 package org.apache.spark.mllib.regression
 
-import scala.collection.JavaConversions._
-import scala.util.Random
 
 import org.jblas.DoubleMatrix
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.FunSuite
 
 import org.apache.spark.SparkContext
-import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.util.LinearDataGenerator
 
 class RidgeRegressionSuite extends FunSuite with BeforeAndAfterAll {