[MLlib] Minor: UDF style update.

Author: Reynold Xin <rxin@databricks.com>

Closes #4388 from rxin/mllib-style and squashes the following commits:

61d465b [Reynold Xin] oops
3364295 [Reynold Xin] Missed one ..
5e068e3 [Reynold Xin] [MLlib] Minor: UDF style update.

2 files changed, 7 insertions, 5 deletions

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 df90078de1..b46a5cd8bd 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
@@ -132,12 +132,14 @@ class LogisticRegressionModel private[ml] (
   override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
     transformSchema(dataset.schema, paramMap, logging = true)
     val map = this.paramMap ++ paramMap
-    val scoreFunction = udf((v: Vector) => {
+    val scoreFunction = udf { v: Vector =>
       val margin = BLAS.dot(v, weights)
       1.0 / (1.0 + math.exp(-margin))
-    } : Double)
+    }
     val t = map(threshold)
-    val predictFunction = udf((score: Double) => { if (score > t) 1.0 else 0.0 } : Double)
+    val predictFunction = udf { score: Double =>
+      if (score > t) 1.0 else 0.0
+    }
     dataset
       .select($"*", scoreFunction(col(map(featuresCol))).as(map(scoreCol)))
       .select($"*", predictFunction(col(map(scoreCol))).as(map(predictionCol)))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 09456b289e..bf5737177c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -129,13 +129,13 @@ class ALSModel private[ml] (
 
     // Register a UDF for DataFrame, and then
     // create a new column named map(predictionCol) by running the predict UDF.
-    val predict = udf((userFeatures: Seq[Float], itemFeatures: Seq[Float]) => {
+    val predict = udf { (userFeatures: Seq[Float], itemFeatures: Seq[Float]) =>
       if (userFeatures != null && itemFeatures != null) {
         blas.sdot(k, userFeatures.toArray, 1, itemFeatures.toArray, 1)
       } else {
         Float.NaN
       }
-    } : Float)
+    }
     dataset
       .join(users, dataset(map(userCol)) === users("id"), "left")
       .join(items, dataset(map(itemCol)) === items("id"), "left")