Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1155#discussion_r14504928
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
---
@@ -0,0 +1,134 @@
+/*
+ * 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.
+ * NB: type Double both for prediction and label is retained
+ * for compatibility with model.predict that returns Double
+ * and MLUtils.loadLibSVMFile that loads class labels as Double
+ *
+ * @param predictionsAndLabels an RDD of (prediction, label) pairs.
+ */
+class MulticlassMetrics(predictionsAndLabels: RDD[(Double, Double)])
extends Logging {
+
+ /* class = category; label = instance of class; prediction = instance of
class */
+
+ private lazy val labelCountByClass =
predictionsAndLabels.values.countByValue()
+ private lazy val labelCount = labelCountByClass.foldLeft(0L){case(sum,
(_, count)) => sum + count}
+ private lazy val tpByClass = predictionsAndLabels.map{ case (prediction,
label) =>
+ (label, if(label == prediction) 1 else 0) }.reduceByKey{_ +
_}.collectAsMap
+ private lazy val fpByClass = 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.
+ * @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 ={
+ val p = precision(label)
+ val r = recall(label)
+ if((p + r) == 0) 0 else 2 * p * r / (p + r)
+ }
+
+ /**
+ * Returns micro-averaged Recall
+ * (equals to microPrecision and microF1measure for multiclass
classifier)
+ * @return microRecall.
+ */
+ lazy val microRecall: Double =
--- End diff --
This is not useful. It gives you the global precision and the method name
"micro" is confusing. We can simply call it `precision()` and remove `micro*`
methods.
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