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Posted to commits@spark.apache.org by rx...@apache.org on 2016/01/10 21:39:00 UTC
spark git commit: [SPARK-12692][BUILD][MLLIB] Scala style: Fix the
style violation (Space before ", " or ":")
Repository: spark
Updated Branches:
refs/heads/master b78e028e3 -> e5904bb5e
[SPARK-12692][BUILD][MLLIB] Scala style: Fix the style violation (Space before "," or ":")
Fix the style violation (space before , and :).
This PR is a followup for #10643.
Author: Kousuke Saruta <sa...@oss.nttdata.co.jp>
Closes #10684 from sarutak/SPARK-12692-followup-mllib.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/e5904bb5
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/e5904bb5
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/e5904bb5
Branch: refs/heads/master
Commit: e5904bb5e7d83b3731b312c40f7904c0511019f5
Parents: b78e028
Author: Kousuke Saruta <sa...@oss.nttdata.co.jp>
Authored: Sun Jan 10 12:38:57 2016 -0800
Committer: Reynold Xin <rx...@databricks.com>
Committed: Sun Jan 10 12:38:57 2016 -0800
----------------------------------------------------------------------
.../spark/examples/mllib/IsotonicRegressionExample.scala | 2 +-
.../org/apache/spark/examples/mllib/NaiveBayesExample.scala | 2 +-
.../apache/spark/examples/mllib/RegressionMetricsExample.scala | 2 +-
.../scala/org/apache/spark/ml/classification/OneVsRest.scala | 4 ++--
.../src/main/scala/org/apache/spark/ml/feature/RFormula.scala | 2 +-
.../scala/org/apache/spark/ml/feature/VectorAssembler.scala | 2 +-
.../scala/org/apache/spark/ml/tree/impl/RandomForest.scala | 4 ++--
.../apache/spark/mllib/clustering/GaussianMixtureModel.scala | 2 +-
mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala | 2 +-
.../main/scala/org/apache/spark/mllib/linalg/Matrices.scala | 2 +-
.../pmml/export/BinaryClassificationPMMLModelExport.scala | 6 +++---
.../apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala | 4 ++--
.../main/scala/org/apache/spark/mllib/tree/DecisionTree.scala | 2 +-
.../main/scala/org/apache/spark/mllib/tree/model/Node.scala | 2 +-
.../org/apache/spark/mllib/util/LinearDataGenerator.scala | 2 +-
.../scala/org/apache/spark/mllib/classification/SVMSuite.scala | 2 +-
.../scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala | 2 +-
17 files changed, 22 insertions(+), 22 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
index 3834ea8..c433663 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
@@ -25,7 +25,7 @@ import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegression
object IsotonicRegressionExample {
- def main(args: Array[String]) : Unit = {
+ def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("IsotonicRegressionExample")
val sc = new SparkContext(conf)
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
index 8bae1b9..0187ad6 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
object NaiveBayesExample {
- def main(args: Array[String]) : Unit = {
+ def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("NaiveBayesExample")
val sc = new SparkContext(conf)
// $example on$
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
----------------------------------------------------------------------
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
index ace16ff..add634c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/RegressionMetricsExample.scala
@@ -27,7 +27,7 @@ import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.sql.SQLContext
object RegressionMetricsExample {
- def main(args: Array[String]) : Unit = {
+ def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("RegressionMetricsExample")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 08a5110..c41a611 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -113,13 +113,13 @@ final class OneVsRestModel private[ml] (
val updateUDF = udf { (predictions: Map[Int, Double], prediction: Vector) =>
predictions + ((index, prediction(1)))
}
- val transformedDataset = model.transform(df).select(columns : _*)
+ val transformedDataset = model.transform(df).select(columns: _*)
val updatedDataset = transformedDataset
.withColumn(tmpColName, updateUDF(col(accColName), col(rawPredictionCol)))
val newColumns = origCols ++ List(col(tmpColName))
// switch out the intermediate column with the accumulator column
- updatedDataset.select(newColumns : _*).withColumnRenamed(tmpColName, accColName)
+ updatedDataset.select(newColumns: _*).withColumnRenamed(tmpColName, accColName)
}
if (handlePersistence) {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index f995243..6cc9d02 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -238,7 +238,7 @@ private class ColumnPruner(columnsToPrune: Set[String]) extends Transformer {
override def transform(dataset: DataFrame): DataFrame = {
val columnsToKeep = dataset.columns.filter(!columnsToPrune.contains(_))
- dataset.select(columnsToKeep.map(dataset.col) : _*)
+ dataset.select(columnsToKeep.map(dataset.col): _*)
}
override def transformSchema(schema: StructType): StructType = {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 0b21565..716bc63 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -102,7 +102,7 @@ class VectorAssembler(override val uid: String)
}
}
- dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol), metadata))
+ dataset.select(col("*"), assembleFunc(struct(args: _*)).as($(outputCol), metadata))
}
override def transformSchema(schema: StructType): StructType = {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
index 6e87302..d3376a7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -474,7 +474,7 @@ private[ml] object RandomForest extends Logging {
val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
- val partitionAggregates : RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
+ val partitionAggregates: RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
// Construct a nodeStatsAggregators array to hold node aggregate stats,
// each node will have a nodeStatsAggregator
@@ -825,7 +825,7 @@ private[ml] object RandomForest extends Logging {
protected[tree] def findSplits(
input: RDD[LabeledPoint],
metadata: DecisionTreeMetadata,
- seed : Long): Array[Array[Split]] = {
+ seed: Long): Array[Array[Split]] = {
logDebug("isMulticlass = " + metadata.isMulticlass)
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 5c9bc62..16bc45b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -177,7 +177,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
}
@Since("1.4.0")
- override def load(sc: SparkContext, path: String) : GaussianMixtureModel = {
+ override def load(sc: SparkContext, path: String): GaussianMixtureModel = {
val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
implicit val formats = DefaultFormats
val k = (metadata \ "k").extract[Int]
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index 5273ed4..ffae0e7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -134,7 +134,7 @@ object FPGrowthModel extends Loader[FPGrowthModel[_]] {
loadImpl(freqItemsets, sample)
}
- def loadImpl[Item : ClassTag](freqItemsets: DataFrame, sample: Item): FPGrowthModel[Item] = {
+ def loadImpl[Item: ClassTag](freqItemsets: DataFrame, sample: Item): FPGrowthModel[Item] = {
val freqItemsetsRDD = freqItemsets.select("items", "freq").map { x =>
val items = x.getAs[Seq[Item]](0).toArray
val freq = x.getLong(1)
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index d7a74db..b08da4f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -279,7 +279,7 @@ class DenseMatrix @Since("1.3.0") (
}
override def hashCode: Int = {
- com.google.common.base.Objects.hashCode(numRows : Integer, numCols: Integer, toArray)
+ com.google.common.base.Objects.hashCode(numRows: Integer, numCols: Integer, toArray)
}
private[mllib] def toBreeze: BM[Double] = {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
index 7abb1bf..a8c32f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala
@@ -27,9 +27,9 @@ import org.apache.spark.mllib.regression.GeneralizedLinearModel
* PMML Model Export for GeneralizedLinearModel class with binary ClassificationModel
*/
private[mllib] class BinaryClassificationPMMLModelExport(
- model : GeneralizedLinearModel,
- description : String,
- normalizationMethod : RegressionNormalizationMethodType,
+ model: GeneralizedLinearModel,
+ description: String,
+ normalizationMethod: RegressionNormalizationMethodType,
threshold: Double)
extends PMMLModelExport {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala
index b5b824b..255c614 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala
@@ -26,14 +26,14 @@ import org.apache.spark.mllib.clustering.KMeansModel
/**
* PMML Model Export for KMeansModel class
*/
-private[mllib] class KMeansPMMLModelExport(model : KMeansModel) extends PMMLModelExport{
+private[mllib] class KMeansPMMLModelExport(model: KMeansModel) extends PMMLModelExport{
populateKMeansPMML(model)
/**
* Export the input KMeansModel model to PMML format.
*/
- private def populateKMeansPMML(model : KMeansModel): Unit = {
+ private def populateKMeansPMML(model: KMeansModel): Unit = {
pmml.getHeader.setDescription("k-means clustering")
if (model.clusterCenters.length > 0) {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index af1f7e7..c73774f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -600,7 +600,7 @@ object DecisionTree extends Serializable with Logging {
val nodeToFeatures = getNodeToFeatures(treeToNodeToIndexInfo)
val nodeToFeaturesBc = input.sparkContext.broadcast(nodeToFeatures)
- val partitionAggregates : RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
+ val partitionAggregates: RDD[(Int, DTStatsAggregator)] = if (nodeIdCache.nonEmpty) {
input.zip(nodeIdCache.get.nodeIdsForInstances).mapPartitions { points =>
// Construct a nodeStatsAggregators array to hold node aggregate stats,
// each node will have a nodeStatsAggregator
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 66f0908..b373c2d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -83,7 +83,7 @@ class Node @Since("1.2.0") (
* @return predicted value
*/
@Since("1.1.0")
- def predict(features: Vector) : Double = {
+ def predict(features: Vector): Double = {
if (isLeaf) {
predict.predict
} else {
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 094528e..240781b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -175,7 +175,7 @@ object LinearDataGenerator {
nfeatures: Int,
eps: Double,
nparts: Int = 2,
- intercept: Double = 0.0) : RDD[LabeledPoint] = {
+ intercept: Double = 0.0): RDD[LabeledPoint] = {
val random = new Random(42)
// Random values distributed uniformly in [-0.5, 0.5]
val w = Array.fill(nfeatures)(random.nextDouble() - 0.5)
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
----------------------------------------------------------------------
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 ee3c85d..1a47344 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
@@ -45,7 +45,7 @@ object SVMSuite {
nPoints: Int,
seed: Int): Seq[LabeledPoint] = {
val rnd = new Random(seed)
- val weightsMat = new DoubleMatrix(1, weights.length, weights : _*)
+ val weightsMat = new DoubleMatrix(1, weights.length, weights: _*)
val x = Array.fill[Array[Double]](nPoints)(
Array.fill[Double](weights.length)(rnd.nextDouble() * 2.0 - 1.0))
val y = x.map { xi =>
http://git-wip-us.apache.org/repos/asf/spark/blob/e5904bb5/mllib/src/test/scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala
----------------------------------------------------------------------
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala
index 1142102..5044181 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/stat/StreamingTestSuite.scala
@@ -27,7 +27,7 @@ import org.apache.spark.util.random.XORShiftRandom
class StreamingTestSuite extends SparkFunSuite with TestSuiteBase {
- override def maxWaitTimeMillis : Int = 30000
+ override def maxWaitTimeMillis: Int = 30000
test("accuracy for null hypothesis using welch t-test") {
// set parameters
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