Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11303#discussion_r53639940
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
---
@@ -129,86 +135,199 @@ class MulticlassMetrics @Since("1.1.0")
(predictionAndLabels: RDD[(Double, Doubl
}
/**
- * Returns f1-measure for a given label (category)
- * @param label the label.
- */
+ * Returns f1-measure for a given label (category)
+ *
+ * @param label the label.
+ */
@Since("1.1.0")
def fMeasure(label: Double): Double = fMeasure(label, 1.0)
/**
- * Returns precision
- */
+ * Returns precision
+ */
@Since("1.1.0")
lazy val precision: Double = tpByClass.values.sum.toDouble / labelCount
/**
- * Returns recall
- * (equals to precision for multiclass classifier
- * because sum of all false positives is equal to sum
- * of all false negatives)
- */
+ * Returns recall
+ * (equals to precision for multiclass classifier
+ * because sum of all false positives is equal to sum
+ * of all false negatives)
+ */
@Since("1.1.0")
lazy val recall: Double = precision
/**
- * Returns f-measure
- * (equals to precision and recall because precision equals recall)
- */
+ * Returns f-measure
+ * (equals to precision and recall because precision equals recall)
+ */
@Since("1.1.0")
lazy val fMeasure: Double = precision
/**
- * Returns weighted true positive rate
- * (equals to precision, recall and f-measure)
- */
+ * Returns weighted true positive rate
+ * (equals to precision, recall and f-measure)
+ */
@Since("1.1.0")
lazy val weightedTruePositiveRate: Double = weightedRecall
/**
- * Returns weighted false positive rate
- */
+ * Returns weighted false positive rate
+ */
@Since("1.1.0")
lazy val weightedFalsePositiveRate: Double = labelCountByClass.map {
case (category, count) =>
falsePositiveRate(category) * count.toDouble / labelCount
}.sum
/**
- * Returns weighted averaged recall
- * (equals to precision, recall and f-measure)
- */
+ * Returns weighted averaged recall
+ * (equals to precision, recall and f-measure)
+ */
@Since("1.1.0")
lazy val weightedRecall: Double = labelCountByClass.map { case
(category, count) =>
recall(category) * count.toDouble / labelCount
}.sum
/**
- * Returns weighted averaged precision
- */
+ * Returns weighted averaged precision
+ */
@Since("1.1.0")
lazy val weightedPrecision: Double = labelCountByClass.map { case
(category, count) =>
precision(category) * count.toDouble / labelCount
}.sum
/**
- * Returns weighted averaged f-measure
- * @param beta the beta parameter.
- */
+ * Returns weighted averaged f-measure
+ *
+ * @param beta the beta parameter.
+ */
@Since("1.1.0")
def weightedFMeasure(beta: Double): Double = labelCountByClass.map {
case (category, count) =>
fMeasure(category, beta) * count.toDouble / labelCount
}.sum
/**
- * Returns weighted averaged f1-measure
- */
+ * Returns weighted averaged f1-measure
+ */
@Since("1.1.0")
lazy val weightedFMeasure: Double = labelCountByClass.map { case
(category, count) =>
fMeasure(category, 1.0) * count.toDouble / labelCount
}.sum
/**
- * Returns the sequence of labels in ascending order
- */
+ * Returns the sequence of labels in ascending order
+ */
@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("1.6.0")
--- End diff --
This can't be right since 1.6.0 is out. 2.0.0.
Your scaladoc indentation is off and you need to not automatically alter
the rest of it. Most of your PR is whitespace change
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