From fa21d9b0a77069587f758b4a80c4ac8d90bbc722 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Mon, 10 Nov 2014 22:55:33 -0800 Subject: [PATCH] update extends indentation --- .../scala/org/apache/spark/ml/Pipeline.scala | 3 ++- .../org/apache/spark/ml/Transformer.scala | 2 +- .../ml/classification/LogisticRegression.scala | 3 ++- .../BinaryClassificationEvaluator.scala | 3 +-- .../spark/ml/feature/StandardScaler.scala | 4 ++-- .../org/apache/spark/ml/param/params.scala | 18 ++++++++++++------ .../spark/ml/tuning/CrossValidator.scala | 3 ++- 7 files changed, 22 insertions(+), 14 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 24ddbed5a046f..8748457bf3f28 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -130,7 +130,8 @@ class Pipeline extends Estimator[PipelineModel] { class PipelineModel( override val parent: Pipeline, override val fittingParamMap: ParamMap, - val transformers: Array[Transformer]) extends Model[PipelineModel] with Logging { + val transformers: Array[Transformer]) + extends Model[PipelineModel] with Logging { /** * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 0236f5f6dfead..71e54fc2d74f4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -84,7 +84,7 @@ abstract class Transformer extends PipelineStage with Params { * result as a new column. */ abstract class UnaryTransformer[IN, OUT: TypeTag, T <: UnaryTransformer[IN, OUT, T]] - extends Transformer with HasInputCol with HasOutputCol with Logging { + extends Transformer with HasInputCol with HasOutputCol with Logging { def setInputCol(value: String): T = { set(inputCol, value); this.asInstanceOf[T] } def setOutputCol(value: String): T = { set(outputCol, value); this.asInstanceOf[T] } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 66e2e000423c7..f6769b530a96e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -113,7 +113,8 @@ class LogisticRegression extends Estimator[LogisticRegressionModel] with Logisti class LogisticRegressionModel private[ml] ( override val parent: LogisticRegression, override val fittingParamMap: ParamMap, - val weights: Vector) extends Model[LogisticRegressionModel] with LogisticRegressionParams { + val weights: Vector) + extends Model[LogisticRegressionModel] with LogisticRegressionParams { def setThreshold(value: Double): this.type = { set(threshold, value); this } def setFeaturesCol(value: String): this.type = { set(featuresCol, value); this } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index dceefa2b56d7d..ec9825f1d35b5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -21,13 +21,12 @@ import org.apache.spark.ml._ import org.apache.spark.ml.param._ import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.sql.{DoubleType, Row, SchemaRDD} -import org.apache.spark.storage.StorageLevel /** * Evaluator for binary classification, which expects two input columns: score and label. */ class BinaryClassificationEvaluator extends Evaluator with Params - with HasScoreCol with HasLabelCol { + with HasScoreCol with HasLabelCol { /** param for metric name in evaluation */ val metricName: Param[String] = new Param(this, "metricName", diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 5b2d07fe9dc31..1a1de84eaafda 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -71,8 +71,8 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP class StandardScalerModel private[ml] ( override val parent: StandardScaler, override val fittingParamMap: ParamMap, - scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] - with StandardScalerParams { + scaler: feature.StandardScalerModel) + extends Model[StandardScalerModel] with StandardScalerParams { def setInputCol(value: String): this.type = { set(inputCol, value); this } def setOutputCol(value: String): this.type = { set(outputCol, value); this } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 8e6a251b55bae..a642af9588697 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -37,7 +37,8 @@ class Param[T] ( val parent: Params, val name: String, val doc: String, - val defaultValue: Option[T] = None) extends Serializable { + val defaultValue: Option[T] = None) + extends Serializable { /** * Creates a param pair with the given value (for Java). @@ -62,31 +63,36 @@ class Param[T] ( /** Specialized version of [[Param[Double]]] for Java. */ class DoubleParam(parent: Params, name: String, doc: String, defaultValue: Option[Double] = None) - extends Param[Double](parent, name, doc, defaultValue) { + extends Param[Double](parent, name, doc, defaultValue) { + override def w(value: Double): ParamPair[Double] = super.w(value) } /** Specialized version of [[Param[Int]]] for Java. */ class IntParam(parent: Params, name: String, doc: String, defaultValue: Option[Int] = None) - extends Param[Int](parent, name, doc, defaultValue) { + extends Param[Int](parent, name, doc, defaultValue) { + override def w(value: Int): ParamPair[Int] = super.w(value) } /** Specialized version of [[Param[Float]]] for Java. */ class FloatParam(parent: Params, name: String, doc: String, defaultValue: Option[Float] = None) - extends Param[Float](parent, name, doc, defaultValue) { + extends Param[Float](parent, name, doc, defaultValue) { + override def w(value: Float): ParamPair[Float] = super.w(value) } /** Specialized version of [[Param[Long]]] for Java. */ class LongParam(parent: Params, name: String, doc: String, defaultValue: Option[Long] = None) - extends Param[Long](parent, name, doc, defaultValue) { + extends Param[Long](parent, name, doc, defaultValue) { + override def w(value: Long): ParamPair[Long] = super.w(value) } /** Specialized version of [[Param[Boolean]]] for Java. */ class BooleanParam(parent: Params, name: String, doc: String, defaultValue: Option[Boolean] = None) - extends Param[Boolean](parent, name, doc, defaultValue) { + extends Param[Boolean](parent, name, doc, defaultValue) { + override def w(value: Boolean): ParamPair[Boolean] = super.w(value) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index d50cfa81982de..c665fa404b37a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -111,7 +111,8 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP class CrossValidatorModel private[ml] ( override val parent: CrossValidator, override val fittingParamMap: ParamMap, - val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams { + val bestModel: Model[_]) + extends Model[CrossValidatorModel] with CrossValidatorParams { override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = { bestModel.transform(dataset, paramMap)