Github user avulanov commented on a diff in the pull request:
https://github.com/apache/spark/pull/1155#discussion_r14870864
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
---
@@ -0,0 +1,183 @@
+/*
+ * 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.Logging
+import org.apache.spark.SparkContext._
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg.{Matrices, Matrix}
+import org.apache.spark.rdd.RDD
+
+import scala.collection.Map
+
+/**
+ * ::Experimental::
+ * Evaluator for multiclass classification.
+ *
+ * @param predictionAndLabels an RDD of (prediction, label) pairs.
+ */
+@Experimental
+class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
+
+ private lazy val labelCountByClass: Map[Double, Long] =
predictionAndLabels.values.countByValue()
+ private lazy val labelCount: Long = labelCountByClass.values.sum
+ private lazy val tpByClass: Map[Double, Int] = predictionAndLabels
+ .map { case (prediction, label) =>
+ (label, if (label == prediction) 1 else 0)
+ }.reduceByKey(_ + _)
+ .collectAsMap()
+ private lazy val fpByClass: Map[Double, Int] = predictionAndLabels
+ .map { case (prediction, label) =>
+ (prediction, if (prediction != label) 1 else 0)
+ }.reduceByKey(_ + _)
+ .collectAsMap()
+ private lazy val confusions = predictionAndLabels.map {
+ case (prediction, label) => ((prediction, label), 1)
+ }.reduceByKey(_ + _).collectAsMap()
+
+ /**
+ * Returns confusion matrix:
+ * predicted classes are in columns,
+ * they are ordered by class label ascending,
+ * as in "labels"
+ */
+ lazy val confusionMatrix: Matrix = {
+ val transposedMatrix = Array.ofDim[Double](labels.size, labels.size)
+ for (i <- 0 to labels.size - 1; j <- 0 to labels.size - 1) {
+ transposedMatrix(i)(j) = confusions.getOrElse((labels(i),
labels(j)), 0).toDouble
+ }
+ val flatMatrix = transposedMatrix.flatMap(arr => arr)
--- End diff --
I removed the intermediate matrix. Could you clarify what do you want me to
do with "for" loop?
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