[predictionio-template-attribute-based-classifier] branch develop updated: Mark override methods

[shimamoto]

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shimamoto pushed a commit to branch develop
in repository 
https://gitbox.apache.org/repos/asf/predictionio-template-attribute-based-classifier.git


The following commit(s) were added to refs/heads/develop by this push:
     new c001288  Mark override methods
     new 918ad91  Merge pull request #16 from takezoe/mark-override
c001288 is described below

commit c00128882d9a1ad81acb74d1f8500b3e5edb6c8a
Author: Naoki Takezoe <take...@apache.org>
AuthorDate: Thu May 30 20:08:40 2019 +0900

    Mark override methods
---
 src/main/scala/Evaluation.scala          | 1 +
 src/main/scala/NaiveBayesAlgorithm.scala | 2 ++
 src/main/scala/PrecisionEvaluation.scala | 1 +
 src/main/scala/Preparator.scala          | 1 +
 4 files changed, 5 insertions(+)

diff --git a/src/main/scala/Evaluation.scala b/src/main/scala/Evaluation.scala
index d17e39e..fc375cb 100644
--- a/src/main/scala/Evaluation.scala
+++ b/src/main/scala/Evaluation.scala
@@ -8,6 +8,7 @@ import org.apache.predictionio.controller.Evaluation
 
 case class Accuracy()
   extends AverageMetric[EmptyEvaluationInfo, Query, PredictedResult, 
ActualResult] {
+  override
   def calculate(query: Query, predicted: PredictedResult, actual: ActualResult)
   : Double = (if (predicted.label == actual.label) 1.0 else 0.0)
 }
diff --git a/src/main/scala/NaiveBayesAlgorithm.scala 
b/src/main/scala/NaiveBayesAlgorithm.scala
index 603a652..527ec70 100644
--- a/src/main/scala/NaiveBayesAlgorithm.scala
+++ b/src/main/scala/NaiveBayesAlgorithm.scala
@@ -20,6 +20,7 @@ class NaiveBayesAlgorithm(val ap: AlgorithmParams)
 
   @transient lazy val logger = Logger[this.type]
 
+  override
   def train(sc: SparkContext, data: PreparedData): NaiveBayesModel = {
     // MLLib NaiveBayes cannot handle empty training data.
     require(data.labeledPoints.take(1).nonEmpty,
@@ -30,6 +31,7 @@ class NaiveBayesAlgorithm(val ap: AlgorithmParams)
     NaiveBayes.train(data.labeledPoints, ap.lambda)
   }
 
+  override
   def predict(model: NaiveBayesModel, query: Query): PredictedResult = {
     val label = model.predict(Vectors.dense(
       Array(query.attr0, query.attr1, query.attr2)
diff --git a/src/main/scala/PrecisionEvaluation.scala 
b/src/main/scala/PrecisionEvaluation.scala
index d0914f1..aebb5a4 100644
--- a/src/main/scala/PrecisionEvaluation.scala
+++ b/src/main/scala/PrecisionEvaluation.scala
@@ -8,6 +8,7 @@ case class Precision(label: Double)
   extends OptionAverageMetric[EmptyEvaluationInfo, Query, PredictedResult, 
ActualResult] {
   override def header: String = s"Precision(label = $label)"
 
+  override
   def calculate(query: Query, predicted: PredictedResult, actual: ActualResult)
   : Option[Double] = {
     if (predicted.label == label) {
diff --git a/src/main/scala/Preparator.scala b/src/main/scala/Preparator.scala
index 880021a..588d654 100644
--- a/src/main/scala/Preparator.scala
+++ b/src/main/scala/Preparator.scala
@@ -12,6 +12,7 @@ class PreparedData(
 
 class Preparator extends PPreparator[TrainingData, PreparedData] {
 
+  override
   def prepare(sc: SparkContext, trainingData: TrainingData): PreparedData = {
     new PreparedData(trainingData.labeledPoints)
   }