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author | Xin Ren <iamshrek@126.com> | 2016-08-30 11:24:55 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-08-30 11:24:55 +0100 |
commit | 27209252f09ff73c58e60c6df8aaba73b308088c (patch) | |
tree | 23eb0a48d34fab230d8a48a0f0303299ace47c0d /mllib/src | |
parent | d4eee9932edf1a489d7fe9120a0f003150834df6 (diff) | |
download | spark-27209252f09ff73c58e60c6df8aaba73b308088c.tar.gz spark-27209252f09ff73c58e60c6df8aaba73b308088c.tar.bz2 spark-27209252f09ff73c58e60c6df8aaba73b308088c.zip |
[MINOR][MLLIB][SQL] Clean up unused variables and unused import
## What changes were proposed in this pull request?
Clean up unused variables and unused import statements, unnecessary `return` and `toArray`, and some more style improvement, when I walk through the code examples.
## How was this patch tested?
Testet manually on local laptop.
Author: Xin Ren <iamshrek@126.com>
Closes #14836 from keypointt/codeWalkThroughML.
Diffstat (limited to 'mllib/src')
5 files changed, 8 insertions, 8 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala index 96d0bdee9e..902f84f862 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala @@ -136,7 +136,7 @@ class Interaction @Since("1.6.0") (@Since("1.6.0") override val uid: String) ext case _: VectorUDT => val attrs = AttributeGroup.fromStructField(f).attributes.getOrElse( throw new SparkException("Vector attributes must be defined for interaction.")) - attrs.map(getNumFeatures).toArray + attrs.map(getNumFeatures) } new FeatureEncoder(numFeatures) }.toArray diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala index 1ea80cb46a..a7992debe6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/IsotonicRegressionWrapper.scala @@ -23,7 +23,7 @@ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ import org.apache.spark.ml.{Pipeline, PipelineModel} -import org.apache.spark.ml.attribute.{AttributeGroup} +import org.apache.spark.ml.attribute.AttributeGroup import org.apache.spark.ml.feature.RFormula import org.apache.spark.ml.regression.{IsotonicRegression, IsotonicRegressionModel} import org.apache.spark.ml.util._ diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala index e79b1f3164..e539deca4b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/stopwatches.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.util import scala.collection.mutable import org.apache.spark.SparkContext -import org.apache.spark.util.LongAccumulator; +import org.apache.spark.util.LongAccumulator /** * Abstract class for stopwatches. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala index 56fb2d33c2..33a1f18bcc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala @@ -164,7 +164,7 @@ object ChiSqSelectorModel extends Loader[ChiSqSelectorModel] { case Row(feature: Int) => (feature) }.collect() - return new ChiSqSelectorModel(features) + new ChiSqSelectorModel(features) } } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala index c2bc1f17cc..6d60136ddc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala @@ -438,10 +438,10 @@ object RandomRDDs { @DeveloperApi @Since("1.6.0") def randomJavaRDD[T]( - jsc: JavaSparkContext, - generator: RandomDataGenerator[T], - size: Long): JavaRDD[T] = { - randomJavaRDD(jsc, generator, size, 0); + jsc: JavaSparkContext, + generator: RandomDataGenerator[T], + size: Long): JavaRDD[T] = { + randomJavaRDD(jsc, generator, size, 0) } // TODO Generate RDD[Vector] from multivariate distributions. |