Issue #2 fix the bug of constant features when building itree

src/main/scala/org/apache/spark/ml/iforest/IForest.scala

@@ -498,6 +498,9 @@ class IForest (
         // calculate actual maxDepth to limit tree height
         val longestPath = Math.ceil(Math.log(Math.max(2, points.length)) / Math.log(2)).toInt
         val possibleMaxDepth = if ($(maxDepth) > longestPath) longestPath else $(maxDepth)
+        if(possibleMaxDepth != $(maxDepth)) {
+          logger.warn("building itree using possible max depth " + possibleMaxDepth + ", instead of " + $(maxDepth))
+        }
 
         val numFeatures = trainData.head.size
         // a array stores constant features index
@@ -559,12 +562,12 @@ class IForest (
   /**
     * Builds a tree
     *
-    * @param data Input data, a two dimensional array, can be regearded as a table, each row
+    * @param data Input data, a two dimensional array, can be regarded as a table, each row
     *             is an instance, each column is a feature value.
     * @param currentPathLength current node's path length
     * @param maxDepth height limit during building a tree
-    * @param constantFeatures an array stores constant features indices, feature
-    *                         will not to draw when it is contant
+    * @param constantFeatures an array stores constant features indices, constant features
+    *                         will not be drawn
     * @return tree's root node
     */
   private[iforest] def iTree(data: Array[Array[Double]],
@@ -588,16 +591,15 @@ class IForest (
       var findConstant = true
       while (findConstant && numFeatures != constantFeatureIndex) {
         // select randomly a feature index
-        attrIndex = constantFeatures(rng.nextInt(numFeatures - constantFeatureIndex) +
-            constantFeatureIndex)
+        val idx = rng.nextInt(numFeatures - constantFeatureIndex) + constantFeatureIndex
+        attrIndex = constantFeatures(idx)
         val features = Array.tabulate(data.length)( i => data(i)(attrIndex))
         attrMin = features.min
         attrMax = features.max
         if (attrMin == attrMax) {
           // swap constant feature index with non-constant feature index
-          val tmp = constantFeatures(attrIndex)
-          constantFeatures(constantFeatureIndex) = tmp
-          constantFeatures(attrIndex) = constantFeatures(constantFeatureIndex)
+          constantFeatures(idx) = constantFeatures(constantFeatureIndex)
+          constantFeatures(constantFeatureIndex) = attrIndex
           // constant feature index add 1, then update
           constantFeatureIndex += 1
           constantFeatures(constantFeatures.length - 1) = constantFeatureIndex

src/test/scala/org/apache/spark/ml/iforest/IForestSuite.scala

@@ -100,12 +100,14 @@ class IForestSuite extends SparkFunSuite with MLlibTestSparkContext with Default
         .setPredictionCol(predictionColName)
         .setAnomalyScoreCol(anomalyScoreName)
         .setContamination(0.2)
+        .setSeed(123L)
     val model = iforest.fit(dataset)
     assert(model.trees.length === 10)
 
     val summary = model.summary
     val anomalies = summary.anomalies.collect
     assert(anomalies.length === 10)
+    // TODO In Spark 2.3.x, function approxQuantile seems to be changed, numAnomalies might be not accurate.
     assert(summary.numAnomalies === 2)
 
     val transformed = model.transform(dataset)