implicit conversion from LabeledPoint to WeightedLabeledPoint

mllib/src/main/scala/org/apache/spark/mllib/point/PointConverter.scala

@@ -0,0 +1,30 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.point
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.regression.LabeledPoint
+
+object PointConverter {
+
+  implicit def LabeledPoint2WeightedLabeledPoint(
+      points : RDD[LabeledPoint]): RDD[WeightedLabeledPoint] = {
+    points.map(point => new WeightedLabeledPoint(point.label,point.features))
+  }
+
+}

mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.tree
 
+import org.apache.spark.mllib.point.PointConverter._
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.Logging
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -211,9 +212,7 @@ object DecisionTree extends Serializable with Logging {
    * @return a DecisionTreeModel that can be used for prediction
   */
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label, x.features))
-    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
   /**
@@ -235,9 +234,7 @@ object DecisionTree extends Serializable with Logging {
       impurity: Impurity,
       maxDepth: Int): DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth)
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label, x.features))
-    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
   /**
@@ -261,9 +258,7 @@ object DecisionTree extends Serializable with Logging {
       maxDepth: Int,
       numClassesForClassification: Int): DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification)
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label, x.features))
-    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
 
@@ -294,9 +289,7 @@ object DecisionTree extends Serializable with Logging {
       labelWeights: Map[Int,Int]): DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification,
       labelWeights = labelWeights)
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label, x.features))
-    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
   /**
@@ -337,9 +330,7 @@ object DecisionTree extends Serializable with Logging {
       categoricalFeaturesInfo: Map[Int,Int]): DecisionTreeModel = {
     val strategy = new Strategy(algo, impurity, maxDepth, numClassesForClassification, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, labelWeights = labelWeights)
-    // Converting from standard instance format to weighted input format for tree training
-    val weightedInput = input.map(x => WeightedLabeledPoint(x.label, x.features))
-    new DecisionTree(strategy).train(weightedInput: RDD[WeightedLabeledPoint])
+    new DecisionTree(strategy).train(input)
   }
 
   private val InvalidBinIndex = -1