Reformat with 0.2.13

repos/PredictionIO/core/src/main/scala/io/prediction/controller/LServing.scala

@@ -17,7 +17,7 @@ package io.prediction.controller
 import io.prediction.annotation.Experimental
 import io.prediction.core.BaseServing
 
-/** Base class of serving. 
+/** Base class of serving.
   *
   * @tparam Q Input query class.
   * @tparam P Output prediction class.

repos/PredictionIO/core/src/main/scala/io/prediction/controller/Metric.scala

@@ -195,7 +195,7 @@ abstract class OptionStdevMetric[EI, Q, P, A]
   }
 }
 
-/** Returns the sum of the score returned by the calculate method. 
+/** Returns the sum of the score returned by the calculate method.
   *
   * @tparam EI Evaluation information
   * @tparam Q Query

repos/PredictionIO/core/src/main/scala/io/prediction/workflow/FakeWorkflow.scala

@@ -70,24 +70,24 @@ private[prediction] case class FakeEvalResult() extends BaseEvaluatorResult {
 }
 
 /** FakeRun allows user to implement custom function under the exact enviroment
-  * as other PredictionIO workflow. 
+  * as other PredictionIO workflow.
   *
-  * Useful for developing new features. Only need to extend this trait and 
-  * implement a function: (SparkContext => Unit). For example, the code below 
+  * Useful for developing new features. Only need to extend this trait and
+  * implement a function: (SparkContext => Unit). For example, the code below
   * can be run with `pio eval HelloWorld`.
   *
   * {{{
   * object HelloWorld extends FakeRun {
   *   // func defines the function pio runs, must have signature (SparkContext => Unit).
   *   func = f
-  * 
+  *
   *   def f(sc: SparkContext): Unit {
   *     val logger = Logger[this.type]
   *     logger.info("HelloWorld")
   *   }
   * }
-  * }}} 
-  * 
+  * }}}
+  *
   */
 @Experimental
 trait FakeRun extends Evaluation with EngineParamsGenerator {

repos/PredictionIO/core/src/main/scala/io/prediction/workflow/Workflow.scala

@@ -39,10 +39,8 @@ object Workflow {
       evaluatorParams: Params,
       env: Map[String, String] = WorkflowUtils.pioEnvVars,
       params: WorkflowParams = WorkflowParams()) {
-
     implicit lazy val formats = Utils.json4sDefaultFormats +
       new NameParamsSerializer
-
     val engineInstance = EngineInstance(
       id = "",
       status = "INIT",
@@ -64,7 +62,6 @@ object Workflow {
       evaluatorResults = "",
       evaluatorResultsHTML = "",
       evaluatorResultsJSON = "")
-
     CoreWorkflow.runEval(
       engine = engine,
       engineParams = engineParams,

repos/PredictionIO/core/src/test/scala/io/prediction/controller/EvaluatorTest.scala

@@ -43,21 +43,18 @@ object TestEvaluator {
 
   /*
   class Evaluator0 extends Evaluator[EvalInfo, Query, Prediction, Actual,
-      (Query, Prediction, Actual), 
+      (Query, Prediction, Actual),
       (EvalInfo, Seq[(Query, Prediction, Actual)]),
       Seq[(EvalInfo, (EvalInfo, Seq[(Query, Prediction, Actual)]))]
       ] {
-
     def evaluateUnit(q: Query, p: Prediction, a: Actual)
     : (Query, Prediction, Actual) = (q, p, a)
-
     def evaluateSet(
-        evalInfo: EvalInfo, 
+        evalInfo: EvalInfo,
         eus: Seq[(Query, Prediction, Actual)])
     : (EvalInfo, Seq[(Query, Prediction, Actual)]) = (evalInfo, eus)
-
     def evaluateAll(
-      input: Seq[(EvalInfo, (EvalInfo, Seq[(Query, Prediction, Actual)]))]) 
+      input: Seq[(EvalInfo, (EvalInfo, Seq[(Query, Prediction, Actual)]))])
     = input
   }
  */
@@ -67,25 +64,19 @@ object TestEvaluator {
 class EvaluatorSuite
 extends FunSuite with Inside with SharedSparkContext {
   import io.prediction.controller.TestEvaluator._
-  @transient lazy val logger = Logger[this.type] 
-
+  @transient lazy val logger = Logger[this.type]
   test("Evaluator.evaluate") {
     val engine = new FakeEngine(1, 3, 10)
     val evaluator = new Evaluator0()
-
     val evalDataSet = engine.eval(sc, null.asInstanceOf[EngineParams])
     val er: Seq[(EvalInfo, (EvalInfo, Seq[(Query, Prediction, Actual)]))] =
       evaluator.evaluateBase(sc, evalDataSet)
-
     evalDataSet.zip(er).map { case (input, output) => {
       val (inputEvalInfo, inputQpaRDD) = input
       val (outputEvalInfo, (outputEvalInfo2, outputQpaSeq)) = output
-
       inputEvalInfo shouldBe outputEvalInfo
       inputEvalInfo shouldBe outputEvalInfo2
-
       val inputQpaSeq: Array[(Query, Prediction, Actual)] = inputQpaRDD.collect
-
       inputQpaSeq.size should be (outputQpaSeq.size)
       // TODO. match inputQpa and outputQpa content.
     }}

repos/PredictionIO/core/src/test/scala/io/prediction/workflow/BaseTest.scala

@@ -1,7 +1,9 @@
 //package org.apache.spark
 package io.prediction.workflow
 
-import _root_.io.netty.util.internal.logging.{Slf4JLoggerFactory, InternalLoggerFactory}
+import _root_.io.netty.util.internal.logging.{
+  Slf4JLoggerFactory, InternalLoggerFactory
+}
 import org.scalatest.BeforeAndAfterAll
 import org.scalatest.BeforeAndAfterEach
 import org.scalatest.Suite

repos/PredictionIO/core/src/test/scala/io/prediction/workflow/EngineWorkflowTest.scala

@@ -0,0 +1 @@
+

repos/PredictionIO/data/src/main/scala/io/prediction/data/storage/BiMap.scala

@@ -129,7 +129,7 @@ object BiMap {
     new BiMap(HashMap(ki: _*))
   }
 
-  /** Create a BiMap[String, Double] from a set of String. The Double index 
+  /** Create a BiMap[String, Double] from a set of String. The Double index
     * starts from 0.
     * @param keys a set of String
     * @return a String to Double BiMap

repos/PredictionIO/data/src/main/scala/io/prediction/data/store/LEventStore.scala

@@ -84,7 +84,7 @@ object LEventStore {
   }
 
   /** Reads events generically. If entityType or entityId is not specified, it
-    * results in table scan. 
+    * results in table scan.
     *
     * @param appName return events of this app
     * @param entityType return events of this entityType

repos/PredictionIO/data/src/main/scala/io/prediction/data/view/LBatchView.scala

@@ -165,21 +165,18 @@ class LBatchView(val appId: Int,
     predicate: Event => Boolean,
     init: T,
     op: (T, Event) => T): Map[String, T] = {
-
     _events
       .filter( predicate(_) )
       .groupBy( _.entityId )
       .mapValues( _.sortBy(_.eventTime.getMillis).foldLeft[T](init)(op))
       .toMap
-
   }
    */
 
   /*
   def groupByEntityOrdered[T](
     predicate: Event => Boolean,
     map: Event => T): Map[String, Seq[T]] = {
-
     _events
       .filter( predicate(_) )
       .groupBy( _.entityId )

repos/PredictionIO/data/src/main/scala/io/prediction/data/webhooks/mailchimp/MailChimpConnector.scala

@@ -75,7 +75,7 @@ private[prediction] object MailChimpConnector extends FormConnector {
     "data[merges][LNAME]": "API",
     "data[merges][INTERESTS]": "Group1,Group2",
     "data[ip_opt]": "10.20.10.30",
-    "data[ip_signup]": "10.20.10.30"    
+    "data[ip_signup]": "10.20.10.30"
      */
 
     // convert to ISO8601 format
@@ -109,7 +109,6 @@ private[prediction] object MailChimpConnector extends FormConnector {
     /*
     "action" will either be "unsub" or "delete".
     The reason will be "manual" unless caused by a spam complaint - then it will be "abuse"
-
     "type": "unsubscribe",
     "fired_at": "2009-03-26 21:40:57",
     "data[action]": "unsub",
@@ -196,11 +195,11 @@ private[prediction] object MailChimpConnector extends FormConnector {
     import org.json4s.JsonDSL._
 
     /*
-    "type": "upemail", 
-    "fired_at": "2009-03-26 22:15:09", 
+    "type": "upemail",
+    "fired_at": "2009-03-26 22:15:09",
     "data[list_id]": "a6b5da1054",
-    "data[new_id]": "51da8c3259", 
-    "data[new_email]": "[email protected]", 
+    "data[new_id]": "51da8c3259",
+    "data[new_email]": "[email protected]",
     "data[old_email]": "[email protected]"
      */
 
@@ -225,8 +224,8 @@ private[prediction] object MailChimpConnector extends FormConnector {
 
     /*
     Reason will be one of "hard" (for hard bounces) or "abuse"
-    "type": "cleaned", 
-    "fired_at": "2009-03-26 22:01:00", 
+    "type": "cleaned",
+    "fired_at": "2009-03-26 22:01:00",
     "data[list_id]": "a6b5da1054",
     "data[campaign_id]": "4fjk2ma9xd",
     "data[reason]": "hard",
@@ -253,12 +252,12 @@ private[prediction] object MailChimpConnector extends FormConnector {
     import org.json4s.JsonDSL._
 
     /*
-    "type": "campaign", 
-    "fired_at": "2009-03-26 21:31:21", 
+    "type": "campaign",
+    "fired_at": "2009-03-26 21:31:21",
     "data[id]": "5aa2102003",
-    "data[subject]": "Test Campaign Subject", 
+    "data[subject]": "Test Campaign Subject",
     "data[status]": "sent",
-    "data[reason]": "", 
+    "data[reason]": "",
     "data[list_id]": "a6b5da1054"
      */
 

repos/PredictionIO/data/src/test/scala/io/prediction/data/webhooks/mailchimp/MailChimpConnectorSpec.scala

@@ -83,7 +83,7 @@ class MailChimpConnectorSpec extends Specification with ConnectorTestUtil {
           "data[merges][EMAIL]" -> "[email protected]",
           "data[merges][FNAME]" -> "MailChimp",
           "data[merges][LNAME]" -> "API",
-          "data[merges][INTERESTS]" -> "Group1,Group2", //optional 
+          "data[merges][INTERESTS]" -> "Group1,Group2", //optional
           "data[ip_opt]" -> "10.20.10.30",
           "data[campaign_id]" -> "cb398d21d2"
       )
@@ -116,7 +116,7 @@ class MailChimpConnectorSpec extends Specification with ConnectorTestUtil {
       check(MailChimpConnector, unsubscribe, expected)
     }
 
-    //check profile update to event Json 
+    //check profile update to event Json
     "convert profile update to event JSON" in {
 
       val profileUpdate = Map(
@@ -158,7 +158,7 @@ class MailChimpConnectorSpec extends Specification with ConnectorTestUtil {
       check(MailChimpConnector, profileUpdate, expected)
     }
 
-    //check email update to event Json 
+    //check email update to event Json
     "convert email update to event JSON" in {
 
       val emailUpdate = Map(
@@ -188,7 +188,7 @@ class MailChimpConnectorSpec extends Specification with ConnectorTestUtil {
       check(MailChimpConnector, emailUpdate, expected)
     }
 
-    //check cleaned email to event Json 
+    //check cleaned email to event Json
     "convert cleaned email to event JSON" in {
 
       val cleanedEmail = Map(
@@ -217,7 +217,7 @@ class MailChimpConnectorSpec extends Specification with ConnectorTestUtil {
       check(MailChimpConnector, cleanedEmail, expected)
     }
 
-    //check campaign sending status to event Json 
+    //check campaign sending status to event Json
     "convert campaign sending status to event JSON" in {
 
       val campaign = Map(

repos/PredictionIO/examples/experimental/scala-cleanup-app/src/main/scala/Preparator.scala

@@ -10,12 +10,10 @@ import org.apache.spark.rdd.RDD
 /*
 class Preparator
   extends PPreparator[TrainingData, PreparedData] {
-
   def prepare(sc: SparkContext, trainingData: TrainingData): PreparedData = {
     new PreparedData(events = trainingData.events)
   }
 }
-
 class PreparedData(
   val events: RDD[Event]
 ) extends Serializable

repos/PredictionIO/examples/experimental/scala-local-friend-recommendation/src/main/scala/FriendRecommendationDataSource.scala

@@ -18,7 +18,7 @@ class FriendRecommendationDataSource(
       readRelationship(dsp.userActionFilePath, userKeyword.size, userIdMap)
     // Originally for the purpose of training an acceptance threshold
     // Commented out here due to the high time and space complexity of training
-    // val trainingRecord = readTrainingRecord(dsp.trainingRecordFilePath, 
+    // val trainingRecord = readTrainingRecord(dsp.trainingRecordFilePath,
     //   userIdMap, itemIdMap)
     val trainingRecord = null
     new FriendRecommendationTrainingData(userIdMap,

repos/PredictionIO/examples/experimental/scala-local-friend-recommendation/src/main/scala/FriendRecommendationTrainingData.scala

@@ -11,7 +11,7 @@ class FriendRecommendationTrainingData(
     // Keyword array, internal id index, term-weight map item
     val userKeyword: Array[HashMap[Int, Double]],
     val itemKeyword: Array[HashMap[Int, Double]],
-    // User relationship array, 
+    // User relationship array,
     // src internal id index, dest-internal-id-weight list item
     val socialAction: Array[List[(Int, Int)]],
     // Training record for training purpose

repos/PredictionIO/examples/experimental/scala-local-regression/Run.scala

@@ -44,10 +44,10 @@ case class LocalDataSource(val dsp: DataSourceParams)
       Source.fromFile(dsp.filepath).getLines.toSeq.map(_.split(" ", 2))
 
     // FIXME: Use different training / testing data.
-    val x = lines.map { _ (1).split(' ').map { _.toDouble } }.map { e =>
+    val x = lines.map { _(1).split(' ').map { _.toDouble } }.map { e =>
       Vector(e: _*)
     }
-    val y = lines.map { _ (0).toDouble }
+    val y = lines.map { _(0).toDouble }
 
     val td = TrainingData(Vector(x: _*), Vector(y: _*))
 

repos/PredictionIO/examples/experimental/scala-parallel-trim-app/src/main/scala/Preparator.scala

@@ -10,12 +10,10 @@ import org.apache.spark.rdd.RDD
 /*
 class Preparator
   extends PPreparator[TrainingData, PreparedData] {
-
   def prepare(sc: SparkContext, trainingData: TrainingData): PreparedData = {
     new PreparedData(events = trainingData.events)
   }
 }
-
 class PreparedData(
   val events: RDD[Event]
 ) extends Serializable

repos/PredictionIO/examples/experimental/scala-refactor-test/src/main/scala/Preparator.scala

@@ -1,23 +1,17 @@
 /*
 package pio.refactor
-
 import io.prediction.controller.PPreparator
 import io.prediction.data.storage.Event
-
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
-
 class Preparator
   extends PPreparator[TrainingData, PreparedData] {
-
   def prepare(sc: SparkContext, trainingData: TrainingData): PreparedData = {
     new PreparedData(events = trainingData.events)
   }
 }
-
 class PreparedData(
   val events: RDD[Event]
 ) extends Serializable
-
  */

repos/PredictionIO/examples/experimental/scala-stock/src/main/scala/Indicators.scala

@@ -20,7 +20,7 @@ import nak.regress.LinearRegression
   *
   * All indicators should be defined as classes that extend
   * this base class. See RSIIndicator as an example. These indicators can then
-  * be instantiated and passed into a StockStrategy class. Refer to tutorial 
+  * be instantiated and passed into a StockStrategy class. Refer to tutorial
   * for further explanation (found in the README.md file).
   */
 @SerialVersionUID(100L)
@@ -34,12 +34,12 @@ abstract class BaseIndicator extends Serializable {
     */
   def getTraining(logPrice: Series[DateTime, Double]): Series[DateTime, Double]
 
-  /** Applies indicator on a window size of the value returned by 
+  /** Applies indicator on a window size of the value returned by
     * getMinWindowSize() and returns the last value in the resulting series to
     * be used for prediction in RegressionStrategy.
     *
     * @param logPrice series of logarithm of all prices for a particular stock
-    * @return the last value in the resulting series from the feature 
+    * @return the last value in the resulting series from the feature
     *           calculation
     */
   def getOne(input: Series[DateTime, Double]): Double
@@ -102,7 +102,7 @@ class RSIIndicator(rsiPeriod: Int = 14) extends BaseIndicator {
 /** Indicator that calcuate differences of closing prices
   *
   * @constructor create an instance of a ShiftsIndicator
-  * @param period number of days between any 2 closing prices to consider for 
+  * @param period number of days between any 2 closing prices to consider for
   *          calculating a return
   */
 class ShiftsIndicator(period: Int) extends BaseIndicator {