upgrade spark to v2.3.x

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>org.apache.spark.ml</groupId>
     <artifactId>spark-iforest</artifactId>
-    <version>2.2.0</version>
+    <version>2.3.0</version>
 
     <properties>
         <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
@@ -18,7 +18,7 @@
         <log4j.version>1.2.17</log4j.version>
         <skipTests>false</skipTests>
         <maven.version>3.3.9</maven.version>
-        <spark.version>2.2.0</spark.version>
+        <spark.version>2.3.0</spark.version>
     </properties>
 
     <dependencies>

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

@@ -89,7 +89,7 @@ class IForestModel (
       Array(1 - $(contamination)), 0)
     // set anomaly instance label 1
     val predictUDF = udf { (anomalyScore: Double) =>
-      if (anomalyScore >= threshold(0)) 1.0 else 0.0
+      if (anomalyScore > threshold(0)) 1.0 else 0.0
     }
     scoreDataset.withColumn($(predictionCol), predictUDF(col($(anomalyScoreCol))))
   }

src/test/resources/log4j.properties

+log4j.rootCategory=WARN, console
+log4j.appender.console=org.apache.log4j.ConsoleAppender
+log4j.appender.console.target=System.err
+log4j.appender.console.layout=org.apache.log4j.PatternLayout
+log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+
+# Settings to quiet third party logs that are too verbose
+log4j.logger.org.eclipse.jetty=WARN
+log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
+log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
+log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO

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

@@ -107,7 +107,6 @@ class IForestSuite extends SparkFunSuite with MLlibTestSparkContext with Default
     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)