Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15435#discussion_r94616155
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -1095,6 +1131,89 @@ private[classification] class MultiClassSummarizer
extends Serializable {
}
/**
+ * :: Experimental ::
+ * Summary of multi-classification algorithms.
+ *
+ * @param predictions [[DataFrame]] produced by model.transform().
+ * @param predictionCol Name for column of prediction in `predictions`.
+ * @param labelCol Name for column of label in `predictions`.
+ */
+@Experimental
+@Since("2.1.0")
+class MulticlassSummary private[ml] (
+ @transient val predictions: DataFrame,
+ val predictionCol: String,
+ val labelCol: String) extends Serializable {
+
+ @transient private val multinomialMetrics = {
+ new MulticlassMetrics(
+ predictions.select(
+ col(predictionCol),
+ col(labelCol).cast(DoubleType))
+ .rdd.map { case Row(prediction: Double, label: Double) =>
(prediction, label) })
+ }
+
+ /** Returns false positive rate for each label. */
+ @Since("2.1.0")
+ @transient lazy val falsePositiveRateByLabel: Array[Double] = {
+ multinomialMetrics.labels.map(label =>
multinomialMetrics.falsePositiveRate(label))
+ }
+
+ /** Returns precision for each label. */
+ @Since("2.1.0")
+ @transient lazy val precisionByLabel: Array[Double] = {
+ multinomialMetrics.labels.map(label =>
multinomialMetrics.precision(label))
+ }
+
+ /** Returns recall for each label. */
+ @Since("2.1.0")
+ @transient lazy val recallByLabel: Array[Double] = {
+ multinomialMetrics.labels.map(label =>
multinomialMetrics.recall(label))
+ }
+
+ /**
+ * Returns f-measure for each label.
+ * @param beta the beta parameter.
+ */
+ @Since("2.1.0")
+ def fMeasureByLabel(beta: Double): Array[Double] = {
+ multinomialMetrics.labels.map(label =>
multinomialMetrics.fMeasure(label, beta))
+ }
+
+ /** Returns f1-measure for each label. */
+ @Since("2.1.0")
+ @transient lazy val fMeasureByLabel: Array[Double] = fMeasureByLabel(1.0)
+
+ /** Returns accuracy. */
+ @Since("2.1.0")
+ @transient lazy val accuracy: Double = multinomialMetrics.accuracy
+
+ /** Returns weighted false positive rate. */
+ @Since("2.1.0")
+ @transient lazy val weightedFalsePositiveRate: Double
+ = multinomialMetrics.weightedFalsePositiveRate
+
+ /** Returns weighted averaged recall. */
+ @Since("2.1.0")
+ @transient lazy val weightedRecall: Double =
multinomialMetrics.weightedRecall
+
+ /** Returns weighted averaged precision. */
+ @Since("2.1.0")
+ @transient lazy val weightedPrecision: Double =
multinomialMetrics.weightedPrecision
+
+ /**
+ * Returns weighted averaged f-measure.
+ * @param beta the beta parameter.
--- End diff --
Same as above.
---
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