srowen commented on a change in pull request #24717: [SPARK-27847][ML] One-Pass
MultilabelMetrics & MulticlassMetrics
URL: https://github.com/apache/spark/pull/24717#discussion_r287793291
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
##########
@@ -179,3 +167,160 @@ class MultilabelMetrics @Since("1.2.0")
(predictionAndLabels: RDD[(Array[Double]
@Since("1.2.0")
lazy val labels: Array[Double] = tpPerClass.keys.toArray.sorted
}
+
+
+/**
+ * Trait for statistical summary for multi-label metrics.
+ */
+private[evaluation] trait MultilabelSummary {
+
+ def numDocs: Long
+
+ def numLabels: Long
+
+ def subsetAccuracy: Double
+
+ def accuracy: Double
+
+ def hammingLoss: Double
+
+ def precision: Double
+
+ def recall: Double
+
+ def f1Measure: Double
+
+ def tpPerClass: Map[Double, Long]
+
+ def fpPerClass: Map[Double, Long]
+
+ def fnPerClass: Map[Double, Long]
+}
+
+
+private[evaluation] class MultilabelSummarizer extends MultilabelSummary with
Serializable {
+
+ private var docCnt = 0L
+ private val labelSet = mutable.Set.empty[Double]
+ private var subsetAccuracyCnt = 0L
+ private var accuracySum = 0.0
+ private var hammingLossSum = 0L
+ private var precisionSum = 0.0
+ private var recallSum = 0.0
+ private var f1MeasureSum = 0.0
+ private val tpPerClass_ = mutable.Map.empty[Double, Long]
+ private val fpPerClass_ = mutable.Map.empty[Double, Long]
+ private val fnPerClass_ = mutable.Map.empty[Double, Long]
+
+ /**
+ * Add a new sample (predictions and labels) to this summarizer, and update
+ * the statistical summary.
+ *
+ * @return This MultilabelSummarizer object.
+ */
+ def add(predictions: Array[Double], labels: Array[Double]): this.type = {
+ val intersection = predictions.intersect(labels)
+
+ docCnt += 1L
+
+ labels.foreach(labelSet.add)
+
+ if (predictions.deep == labels.deep) {
Review comment:
I'd switch this to `Arrays.equals()` for slightly better performance, as I
suppose that's the goal here.
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