From d535d62e4518e5054a2784ee29ea478f471a67b9 Mon Sep 17 00:00:00 2001
From: unknown <ulanov@ULANOV1.emea.hpqcorp.net>
Date: Thu, 19 Jun 2014 19:39:03 +0400
Subject: [PATCH] Multiclass evaluation

---
 .../evaluation/MulticlassEvaluator.scala      | 112 ++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassEvaluator.scala

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassEvaluator.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassEvaluator.scala
new file mode 100644
index 0000000000000..4f52d5e7c301f
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassEvaluator.scala
@@ -0,0 +1,112 @@
+/*
+ * 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.rdd.RDD
+import org.apache.spark.Logging
+import org.apache.spark.SparkContext._
+
+/**
+ * Evaluator for multiclass classification.
+ *
+ * @param scoreAndLabels an RDD of (score, label) pairs.
+ */
+class MulticlassEvaluator(scoreAndLabels: RDD[(Double, Double)]) extends Logging {
+
+  /* class = category; label = instance of class; prediction = instance of class */
+
+  private lazy val labelCountByClass = scoreAndLabels.values.countByValue()
+  private lazy val labelCount = labelCountByClass.foldLeft(0L){case(sum, (_, count)) => sum + count}
+  private lazy val tpByClass = scoreAndLabels.map{ case (prediction, label) => (label, if(label == prediction) 1 else 0) }.reduceByKey{_ + _}.collectAsMap
+  private lazy val fpByClass = scoreAndLabels.map{ case (prediction, label) => (prediction, if(prediction != label) 1 else 0) }.reduceByKey{_ + _}.collectAsMap
+
+  /**
+   * Returns Precision for a given label (category)
+   * @param label the label.
+   * @return Precision.
+   */
+  def precision(label: Double): Double = if(tpByClass(label) + fpByClass.getOrElse(label, 0) == 0) 0
+    else tpByClass(label).toDouble / (tpByClass(label) + fpByClass.getOrElse(label, 0)).toDouble
+
+  /**
+   * Returns Recall for a given label (category)
+   * @param label the label.
+   * @return Recall.
+   */
+  def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label).toDouble
+
+  /**
+   * Returns F1-measure for a given label (category)
+   * @param label the label.
+   * @return F1-measure.*/
+  def f1Measure(label: Double): Double = 2 * precision(label) * recall(label) / (precision(label) + recall(label))
+
+  /**
+   * Returns micro-averaged Recall (equals to microPrecision and microF1measure for multiclass classifier)
+   * @return microRecall.
+   */
+  def microRecall: Double = tpByClass.foldLeft(0L){case (sum,(_, tp)) => sum + tp}.toDouble / labelCount.toDouble
+
+  /**
+   * Returns micro-averaged Precision (equals to microPrecision and microF1measure for multiclass classifier)
+   * @return microPrecision.
+   */
+  def microPrecision: Double = microRecall
+
+  /**
+   * Returns micro-averaged F1-measure (equals to microPrecision and microRecall for multiclass classifier)
+   * @return microF1measure.
+   */
+  def microF1Measure: Double = microRecall
+
+  /**
+   * Returns weighted averaged Recall
+   * @return weightedRecall.
+   */
+  def weightedRecall: Double = labelCountByClass.foldLeft(0.0){case(wRecall, (category, count)) => wRecall + recall(category) * count.toDouble / labelCount.toDouble}
+
+  /**
+   * Returns weighted averaged Precision
+   * @return weightedPrecision.
+   */
+  def weightedPrecision: Double = labelCountByClass.foldLeft(0.0){case(wPrecision, (category, count)) => wPrecision + precision(category) * count.toDouble / labelCount.toDouble}
+
+  /**
+   * Returns weighted averaged F1-measure
+   * @return weightedF1Measure.
+   */
+  def weightedF1Measure: Double = 2 * weightedPrecision * weightedRecall / (weightedPrecision + weightedRecall)
+
+  /**
+   * Returns map with Precisions for individual classes
+   * @return precisionPerClass.
+   */
+  def precisionPerClass = labelCountByClass.map{case (category, _) => (category, precision(category))}.toMap
+
+  /**
+   * Returns map with Recalls for individual classes
+   * @return recallPerClass.
+   */
+  def recallPerClass = labelCountByClass.map{case (category, _) => (category, recall(category))}.toMap
+
+  /**
+   * Returns map with F1-measures for individual classes
+   * @return f1MeasurePerClass.
+   */
+  def f1MeasurePerClass = labelCountByClass.map{case (category, _) => (category, f1Measure(category))}.toMap
+}
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