Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/160#discussion_r10734415
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/classification/BinaryClassificationModel.scala
---
@@ -0,0 +1,68 @@
+/*
+ * 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.classification
+
+
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.regression._
+import org.apache.spark.rdd.RDD
+
+trait BinaryClassificationModel extends ClassificationModel {
+ /**
+ * Return true labels and prediction scores in an RDD
+ *
+ * @param input RDD with labelled points to use for the evaluation
+ * @return RDD[(Double, Double)] Contains a pair of (label, probability)
+ * where probability is the probability the model assigns to
+ * the label being 1.
+ */
+ def scoreForEval(input: RDD[LabeledPoint]) : RDD[(Double, Double)] = {
+ val predictionAndLabel = input.map { point =>
+ val scores = score(point.features)
+ (scores, point.label)
+ }
+ predictionAndLabel
+ }
+
+ /**
+ * Evaluate the performance of the model using the score assigned by the
model
+ * to observations and the true label.
+ * Returns the Receiver operating characteristic area under the curve.
+ * Note that we consider the prediction of a label to be 0 if the score
is less than 0,
+ * and we predict label 1 if the score is larger than 0.
+ *
+ * @param predictionAndLabel RDD with (score by model, true label)
+ * @return Double Area under curve of ROC
+ */
+ def areaUnderROC(predictionAndLabel: RDD[(Double, Double)]) : Double = {
+ val nObs = predictionAndLabel.count.toDouble
+ val nPos = predictionAndLabel.filter(x => x._2 == 1.0).count.toDouble
+ // sort according to the predicted score and add indices
+ val sortedPredictionsWithIndex =
predictionAndLabel.sortByKey(true).zipWithIndex
+ // sum of the positive ranks
+ val sumPosRanks = sortedPredictionsWithIndex.filter(x => (x._1)._2 >
0).map(x => x._2 + 1).sum
--- End diff --
Unless I'm misreading, in this line, positive is defined as "> 0" but it's
defined as "== 1" above. The latter might be the safer definition.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
