Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1367#discussion_r15137023
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/SpearmansCorrelation.scala
---
@@ -0,0 +1,128 @@
+/*
+ * 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 scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.{Logging, HashPartitioner}
+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
+ */
+private[stat] object SpearmansCorrelation extends Correlation with Logging
{
+
+ /**
+ * 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.
+ *
+ * Input RDD[Vector] should be cached or checkpointed if possible since
it would be split into
+ * numCol RDD[Double]s, each of which sorted, and the joined back into a
single RDD[Vector].
+ */
+ override def computeCorrelationMatrix(X: RDD[Vector]): Matrix = {
+ val indexed = X.zipWithUniqueId()
+
+ val numCols = X.first.size
+ if (numCols > 50) {
+ logWarning("Computing the Spearman correlation matrix can be slow
for large RDDs with more"
+ + " than 50 columns.")
+ }
+ 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, X)
+ 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]) = [2.5, 1.0, 0.0, 2.5]
+ * Note that positions here are 0-indexed, instead of the 1-indexed as
in the definition for
+ * ranks in the standard definition for Spearman's correlation. This
does not affect the final
+ * results and is slightly more performant.
+ *
+ * @param indexed RDD[(Double, Long)] containing pairs of the format
(originalValue, uniqueId)
+ * @return RDD[(Long, Double)] containing pairs of the format (uniqueId,
rank), where uniqueId is
+ * copied from the input RDD.
+ */
+ 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 ranks: RDD[(Long, Double)] = sorted.mapPartitions { iter =>
+ // add an extra element to signify the end of the list so that
flatMap can flush the last
+ // batch of duplicates
+ val padded = iter ++
+ Iterator[((Double, Long), Long)](((Double.NaN, Long.MinValue),
Long.MinValue))
+ var lastVal = 0.0
+ var firstRank = 0.0
+ val IDBuffer = new ArrayBuffer[Long]()
+ padded.flatMap { item =>
+ val rank = item._2
+ if (item._1._1 == lastVal && item._2 != Long.MinValue) {
+ IDBuffer += item._1._2
+ Iterator.empty
+ } else {
+ val entries = if (IDBuffer.size == 0) {
+ Iterator.empty
+ } else if (IDBuffer.size == 1) {
+ Iterator((IDBuffer(0), firstRank))
+ } else {
+ // averageRank = ((firstRank + IDBuffer.size) / 2.0) /
IDBuffer.size
--- End diff --
I think this comment could be removed since the line below if not very hard
to understand.
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