Github user dorx commented on a diff in the pull request:
https://github.com/apache/spark/pull/1367#discussion_r15036448
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
---
@@ -0,0 +1,121 @@
+/*
+ * 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.mllib.linalg.{DenseVector, Matrix, Vector}
+import org.apache.spark.rdd.RDD
+
+/**
+ * New correlation algorithms should implement this trait
+ */
+trait Correlation {
+
+ /**
+ * Compute correlation for two datasets.
+ */
+ def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double
+
+ /**
+ * Compute the correlation matrix S, for the input matrix, where S(i, j)
is the correlation
+ * between column i and j.
+ */
+ def computeCorrelationMatrix(X: RDD[Vector]): Matrix
+
+ /**
+ * Combine the two input RDD[Double]s into an RDD[Vector] and compute
the correlation using the
+ * correlation implementation for RDD[Vector]
+ */
+ def computeCorrelationWithMatrixImpl(x: RDD[Double], y: RDD[Double]):
Double = {
+ val mat: RDD[Vector] = x.zip(y).mapPartitions({ iter =>
--- End diff --
Did that before my last comment:
Here's zip:
def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = {
zipPartitions(other, true) { (thisIter, otherIter) =>
new Iterator[(T, U)] {
def hasNext = (thisIter.hasNext, otherIter.hasNext) match {
case (true, true) => true
case (false, false) => false
case _ => throw new SparkException("Can only zip RDDs with " +
"same number of elements in each partition")
}
def next = (thisIter.next, otherIter.next)
}
}
}
zipPartitions here returns new ZippedPartitionsRDD2(sc, sc.clean(f), this,
rdd2, preservesPartitioning), which extends ZippedPartitionsBaseRDD, which has
the following:
override val partitioner =
if (preservesPartitioning) firstParent[Any].partitioner else None
So yes, the partitioner is propagated from the parent via zip.
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