Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/268#discussion_r11196822
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/rdd/VectorRDDFunctionsSuite.scala
---
@@ -0,0 +1,87 @@
+/*
+ * 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.rdd
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.MLUtils._
+
+/**
+ * Test suite for the summary statistics of RDD[Vector]. Both the accuracy
and the time consuming
+ * between dense and sparse vector are tested.
+ */
+class VectorRDDFunctionsSuite extends FunSuite with LocalSparkContext {
+ import VectorRDDFunctionsSuite._
+
+ val localData = Array(
+ Vectors.dense(1.0, 2.0, 3.0),
+ Vectors.dense(4.0, 5.0, 6.0),
+ Vectors.dense(7.0, 8.0, 9.0)
+ )
+
+ val sparseData = ArrayBuffer(Vectors.sparse(20, Seq((0, 1.0), (9, 2.0),
(10, 7.0))))
+ for (i <- 0 until 10000) sparseData += Vectors.sparse(20, Seq((9, 0.0)))
+ sparseData += Vectors.sparse(20, Seq((0, 5.0), (9, 13.0), (16, 2.0)))
+ sparseData += Vectors.sparse(20, Seq((3, 5.0), (9, 13.0), (18, 2.0)))
+
+ test("full-statistics") {
+ val data = sc.parallelize(localData, 2)
+ val (VectorRDDStatisticalAggregator(mean, variance, cnt, nnz, max,
min), denseTime) =
+ time(data.summarizeStatistics())
+
+ assert(equivVector(Vectors.fromBreeze(mean), Vectors.dense(4.0, 5.0,
6.0)),
+ "Column mean do not match.")
+ assert(equivVector(Vectors.fromBreeze(variance), Vectors.dense(6.0,
6.0, 6.0)),
+ "Column variance do not match.")
+ assert(cnt === 3.0, "Column cnt do not match.")
+ assert(equivVector(Vectors.fromBreeze(nnz), Vectors.dense(3.0, 3.0,
3.0)),
+ "Column nnz do not match.")
+ assert(equivVector(Vectors.fromBreeze(max), Vectors.dense(7.0, 8.0,
9.0)),
+ "Column max do not match.")
+ assert(equivVector(Vectors.fromBreeze(min), Vectors.dense(1.0, 2.0,
3.0)),
+ "Column min do not match.")
+
+ val dataForSparse = sc.parallelize(sparseData.toSeq, 2)
+ val (_, sparseTime) = time(dataForSparse.summarizeStatistics())
+
+ println(s"dense time is $denseTime, sparse time is $sparseTime.")
+ assert(relativeTime(denseTime, sparseTime),
--- End diff --
If I do both "remove the `println()`" and "do not `assert()` on running
times", then I can also remove the computing of `denseTime` and `sparseTime`.
How to assert the running time for computing statistical summary of sparse
vectors to be O(nnz) but not O(n d) in this way?
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