Mark override methods
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 @@
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 @@
@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 @@
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 @@
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 Preparator extends PPreparator[TrainingData, PreparedData] {
+ override
def prepare(sc: SparkContext, trainingData: TrainingData): PreparedData = {
new PreparedData(trainingData.labeledPoints)
}