Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11303#discussion_r53767196
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
---
@@ -211,4 +211,119 @@ class MulticlassMetrics @Since("1.1.0")
(predictionAndLabels: RDD[(Double, Doubl
*/
@Since("1.1.0")
lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted
+
+ /**
+ * Returns unweighted Cohen's Kappa
+ * Cohen's kappa coefficient is a statistic which measures inter-rater
+ * agreement for qualitative (categorical) items. It is generally thought
+ * to be a more robust measure than simple percent agreement calculation,
+ * since kappa takes into account the agreement occurring by chance.
+ * The kappa score is a number between -1 and 1. Scores above 0.8 are
+ * generally considered good agreement; zero or lower means no agreement
+ * (practically random labels).
+ */
+ @Since("2.0.0")
+ def kappa(): Double = {
+ kappa("default")
+ }
+
+ /**
+ * Returns Cohen's Kappa with built-in weighted types
+ * @param weights the weighted type. "default" means no weighted;
+ * "linear" means linear weighted;
+ * "quadratic" means quadratic weighted.
+ */
+ @Since("2.0.0")
+ def kappa(weights: String): Double = {
+
+ val func = weights match {
+ // standard kappa without weighting
+ case "default" =>
+ (i: Int, j: Int) => {
+ if (i == j) {
+ 0.0
+ } else {
+ 1.0
+ }
+ }
+ // linear weighted kappa
+ case "linear" =>
+ (i: Int, j: Int) =>
+ math.abs(i - j).toDouble
+ // quadratic weighted kappa
+ case "quadratic" =>
+ (i: Int, j: Int) => {
+ val d = i - j
+ d.toDouble * d
+ }
+ // unknown weighting type
+ case t =>
+ throw new IllegalArgumentException(
+ s"kappa only supports weighting type {linear, quadratic,
default} but got type ${t}.")
+ }
+
+ kappa(func)
+ }
+
+
+ /**
+ * Returns Cohen's Kappa with user-defined weight matrix
+ * @param weights the weight matrix, must be of the same shape with
Confusion Matrix.
+ * Note: Each Element in it must be no less than zero.
+ */
+ @Since("2.0.0")
+ def kappa(weights: Matrix): Double = {
--- End diff --
I still don't think we should expose this. I can be private.
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