Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1155#discussion_r14676283
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
---
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.evaluation
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.rdd.RDD
+import org.apache.spark.Logging
+import org.apache.spark.SparkContext._
+
+import scala.collection.Map
+
+/**
+ * ::Experimental::
+ * Evaluator for multiclass classification.
+ *
+ * @param predictionsAndLabels an RDD of (prediction, label) pairs.
+ */
+@Experimental
+class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)])
extends Logging {
+
+ private lazy val labelCountByClass: Map[Double, Long] =
predictionsAndLabels.values.countByValue()
+ private lazy val labelCount: Long = labelCountByClass.values.sum
+ private lazy val tpByClass: Map[Double, Int] = predictionsAndLabels
+ .map { case (prediction, label) =>
+ (label, if (label == prediction) 1 else 0)
+ }.reduceByKey(_ + _)
+ .collectAsMap()
+ private lazy val fpByClass: Map[Double, Int] = predictionsAndLabels
+ .map { case (prediction, label) =>
+ (prediction, if (prediction != label) 1 else 0)
+ }.reduceByKey(_ + _)
+ .collectAsMap()
+
+ /**
+ * Returns precision for a given label (category)
+ * @param label the label.
+ */
+ def precision(label: Double): Double = {
+ val tp = tpByClass(label)
+ val fp = fpByClass.getOrElse(label, 0)
+ if (tp + fp == 0) 0 else tp.toDouble / (tp + fp)
+ }
+
+ /**
+ * Returns recall for a given label (category)
+ * @param label the label.
+ */
+ def recall(label: Double): Double = tpByClass(label).toDouble /
labelCountByClass(label)
+
+ /**
+ * Returns f-measure for a given label (category)
+ * @param label the label.
+ */
+ def fMeasure(label: Double, beta: Double): Double = {
+ val p = precision(label)
+ val r = recall(label)
+ val betaSqrd = beta * beta
+ if (p + r == 0) 0 else (1 + betaSqrd) * p * r / (betaSqrd * p + r)
+ }
+
+ /**
+ * Returns f1-measure for a given label (category)
+ * @param label the label.
+ */
+ def fMeasure(label: Double): Double = fMeasure(label, 1.0)
+
+ /**
+ * Returns precision
+ */
+ 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)
+ */
+ lazy val recall: Double = precision
+
+ /**
+ * Returns f-measure
+ * (equals to precision and recall because precision equals recall)
+ */
+ lazy val fMeasure: Double = precision
+
+ /**
+ * Returns weighted averaged recall
+ * (equals to precision, recall and f-measure)
+ */
+ lazy val weightedRecall: Double = labelCountByClass.map { case
(category, count) =>
+ recall(category) * count.toDouble / labelCount
+ }.sum
+
+ /**
+ * Returns weighted averaged precision
+ */
+ lazy val weightedPrecision: Double = labelCountByClass.map { case
(category, count) =>
+ precision(category) * count.toDouble / labelCount
+ }.sum
+
+ /**
+ * Returns weighted averaged f1-measure
+ */
+ lazy val weightedF1Measure: Double = labelCountByClass.map { case
(category, count) =>
--- End diff --
Could you make this one take an optional beta parameter as well? Maybe
change the name to `weightedFMeasure` and implement both `weightedFMeasure()`
and `weightedFMeasure(beta)`?
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