Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1367#discussion_r14855906
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmansCorrelation.scala
---
@@ -0,0 +1,102 @@
+/*
+ * 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.stat.correlation
+
+import org.apache.spark.Partitioner
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.linalg.{DenseVector, Matrix, Vector}
+import org.apache.spark.rdd.{CoGroupedRDD, RDD}
+
+/**
+ * Compute Spearman's correlation for two RDDs of the type RDD[Double] or
the correlation matrix
+ * for an RDD of the type RDD[Vector].
+ *
+ * Definition of Spearman's correlation can be found at
+ * http://en.wikipedia.org/wiki/Spearman's_rank_correlation_coefficient
+ */
+object SpearmansCorrelation extends Correlation {
+
+ /**
+ * Compute Spearman's correlation for two datasets.
+ */
+ override def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double
= {
+ computeCorrelationWithMatrixImpl(x, y)
+ }
+
+ /**
+ * Compute Spearman's correlation matrix S, for the input matrix, where
S(i, j) is the
+ * correlation between column i and j.
+ */
+ override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
+ val indexed = X.zipWithIndex()
+ // Attempt to checkpoint the RDD before splitting it into numCols
RDD[Double]s to avoid
+ // computing the lineage prefix multiple times.
+ // If checkpoint directory not set, cache the RDD instead.
+ try {
+ indexed.checkpoint()
+ } catch {
+ case e: Exception => indexed.cache()
+ }
+
+ val numCols = X.first.size
+ val ranks = new Array[RDD[(Long, Double)]](numCols)
+
+ // Note: we use a for loop here instead of a while loop with a single
index variable
+ // to avoid race condition caused by closure serialization
+ for (k <- 0 until numCols) {
+ val column = indexed.map {case(vector, index) => {
+ (vector(k), index)}
+ }
+ ranks(k) = getRanks(column)
+ }
+
+ val ranksMat: RDD[Vector] = makeRankMatrix(ranks)
+ PearsonCorrelation.computeCorrelationMatrix(ranksMat)
+ }
+
+ /**
+ * Compute the ranks for elements in the input RDD, using the average
method for ties.
+ *
+ * With the average method, elements with the same value receive the
same rank that's computed
+ * by taking the average of their positions in the sorted list.
+ * e.g. ranks([2, 1, 0, 2]) = [3.5, 2.0, 1.0, 3.5]
+ */
+ private def getRanks(indexed: RDD[(Double, Long)]): RDD[(Long, Double)]
= {
+ // Get elements' positions in the sorted list for computing average
rank for duplicate values
+ val sorted = indexed.sortByKey().zipWithIndex()
+ val groupedByValue = sorted.groupBy(_._1._1)
+ val ranks = groupedByValue.flatMap[(Long, Double)] { item =>
+ val duplicates = item._2
+ if (duplicates.size > 1) {
+ val averageRank = duplicates.foldLeft(0L) {_ + _._2 + 1} /
duplicates.size.toDouble
+ duplicates.map(entry => (entry._1._2, averageRank)).toSeq
+ } else {
+ duplicates.map(entry => (entry._1._2, entry._2.toDouble + 1)).toSeq
+ }
+ }
+ ranks.sortByKey()
+ }
+
+ private def makeRankMatrix(ranks: Array[RDD[(Long, Double)]]):
RDD[Vector] = {
+ val partitioner = Partitioner.defaultPartitioner(ranks(0), ranks.tail:
_*)
--- End diff --
`ranks(i)` doesn't have a partitioner. So this should fall back to a
default `HashPartitioner` with defaultParallelism. If that is the case, please
use `HashPartitioner` directly to avoid confusion.
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