Github user BaiGang commented on a diff in the pull request:
https://github.com/apache/spark/pull/1270#discussion_r15328629
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
---
@@ -0,0 +1,156 @@
+/*
+ * 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.SparkContext._
+
+/**
+ * Evaluator for multilabel classification.
+ * @param predictionAndLabels an RDD of (predictions, labels) pairs, both
are non-null sets.
+ */
+class MultilabelMetrics(predictionAndLabels: RDD[(Set[Double],
Set[Double])]) {
--- End diff --
Another feasible representation of predictions/labels is
mllib.linalg.Vector. It's basically a Vector of +1s and -1s, either dense or
sparse. So it will be great if we add another function to do the
transformation.
It's up to you. Transforming the data outside this evaluation module is
also OK. : )
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