Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/1367#discussion_r14808804 --- 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 => + iter.map {case(xi, yi) => new DenseVector(Array(xi, yi))} + }, preservesPartitioning = true) + computeCorrelationMatrix(mat)(0, 1) + } + +} + +/** + * Delegates computation to the specific correlation object based on the input method name + * + * Currently supported correlations: pearson, spearman. + * After new correlation algorithms are added, please update the documentation here and in + * Statistics.scala for the correlation APIs. + * + * Cases are ignored when doing method matching. We also allow initials, e.g. "P" for "pearson", as + * long as initials are unique in the supported set of correlation algorithms. In addition, a --- End diff -- This feature actually makes it error-prone to add new correlations. For example, I use `corr(x, y, "p")` in my code and in v1.2 someone adds another correlation starting with "p". Then it breaks existing code. I saw R implements this feature but it simply limits the methods to pearson, spearman, and kendall. If they plan to add new correlation with prefix collision, they will face the same problem.
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