From 93ef9b6a2aa1830170cb101f191022f2dda62c41 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 4 Jan 2016 13:32:14 -0800
Subject: [SPARK-9622][ML] DecisionTreeRegressor: provide variance of
 prediction

DecisionTreeRegressor will provide variance of prediction as a Double column.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8866 from yanboliang/spark-9622.
---
 .../ml/regression/DecisionTreeRegressorSuite.scala | 26 +++++++++++++++++++++-
 1 file changed, 25 insertions(+), 1 deletion(-)

(limited to 'mllib/src/test')

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
index 6999a910c3..0b39af5543 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
@@ -20,12 +20,13 @@ package org.apache.spark.ml.regression
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.util.MLTestingUtils
+import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree,
   DecisionTreeSuite => OldDecisionTreeSuite}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{Row, DataFrame}
 
 
 class DecisionTreeRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
@@ -73,6 +74,29 @@ class DecisionTreeRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     MLTestingUtils.checkCopy(model)
   }
 
+  test("predictVariance") {
+    val dt = new DecisionTreeRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(2)
+      .setMaxBins(100)
+      .setPredictionCol("")
+      .setVarianceCol("variance")
+    val categoricalFeatures = Map(0 -> 2, 1 -> 2)
+
+    val df = TreeTests.setMetadata(categoricalDataPointsRDD, categoricalFeatures, numClasses = 0)
+    val model = dt.fit(df)
+
+    val predictions = model.transform(df)
+      .select(model.getFeaturesCol, model.getVarianceCol)
+      .collect()
+
+    predictions.foreach { case Row(features: Vector, variance: Double) =>
+      val expectedVariance = model.rootNode.predictImpl(features).impurityStats.calculate()
+      assert(variance === expectedVariance,
+        s"Expected variance $expectedVariance but got $variance.")
+    }
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
-- 
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