Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/14321#discussion_r74426625
--- Diff:
core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala ---
@@ -168,6 +168,126 @@ class PairRDDFunctionsSuite extends SparkFunSuite
with SharedSparkContext {
}
}
+ test("randomSplitByKey exact") {
+ val defaultSeed = 1L
+
+ // vary RDD size
+ for (n <- List(100, 1000, 10000)) {
+ val data = sc.parallelize(1 to n, 2)
+ val fractionPositive = 0.3
+ val stratifiedData =
data.keyBy(StratifiedAuxiliary.stratifier(fractionPositive))
+ val keys = Array("0", "1")
+ val splitWeights = Array(0.3, 0.2, 0.5)
+ val weights: Array[scala.collection.Map[String, Double]] =
+ splitWeights.map(w => keys.map(k => (k, w)).toMap)
+ StratifiedAuxiliary.testSplits(stratifiedData, weights, defaultSeed,
n, true)
+ }
+
+ // vary fractionPositive
+ for (fractionPositive <- List(0.1, 0.3, 0.5, 0.7, 0.9)) {
+ val n = 100
+ val data = sc.parallelize(1 to n, 2)
+ val stratifiedData =
data.keyBy(StratifiedAuxiliary.stratifier(fractionPositive))
+ val keys = Array("0", "1")
+ val splitWeights = Array(0.3, 0.2, 0.5)
+ val weights: Array[scala.collection.Map[String, Double]] =
+ splitWeights.map(w => keys.map(k => (k, w)).toMap)
+ StratifiedAuxiliary.testSplits(stratifiedData, weights, defaultSeed,
n, true)
+ }
+
+ // use same data for remaining tests
+ val n = 100
+ val fractionPositive = 0.3
+ val data = sc.parallelize(1 to n, 2)
+ val stratifiedData =
data.keyBy(StratifiedAuxiliary.stratifier(fractionPositive))
+ val keys = Array("0", "1")
+
+ // use different weights for each key in the split
+ val unevenWeights: Array[scala.collection.Map[String, Double]] =
+ Array(Map("0" -> 0.2, "1" -> 0.3), Map("0" -> 0.1, "1" -> 0.4),
Map("0" -> 0.7, "1" -> 0.3))
+ StratifiedAuxiliary.testSplits(stratifiedData, unevenWeights,
defaultSeed, n, true)
+
+ // vary the seed
+ val splitWeights = Array(0.3, 0.2, 0.5)
+ val weights: Array[scala.collection.Map[String, Double]] =
+ splitWeights.map(w => keys.map(k => (k, w)).toMap)
+ for (seed <- defaultSeed to defaultSeed + 3L) {
+ StratifiedAuxiliary.testSplits(stratifiedData, weights, seed, n,
true)
+ }
+
+ // vary the number of splits
+ for (numSplits <- 1 to 3) {
+ val splitWeights = Array.fill(numSplits)(1.0) // check normalization
too
+ val weights: Array[scala.collection.Map[String, Double]] =
+ splitWeights.map(w => keys.map(k => (k, w)).toMap)
+ StratifiedAuxiliary.testSplits(stratifiedData, weights, defaultSeed,
n, true)
+ }
+ val thrown = intercept[IllegalArgumentException] {
+
stratifiedData.randomSplitByKey(Array.empty[scala.collection.Map[String,
Double]], true, 42L)
+ }
+ assert(thrown.getMessage.contains("weights cannot be empty"))
+ }
+
+ test("randomSplitByKey") {
--- End diff --
Am I correct that the only real difference between the tests for this and
the `exact` case are the calls to `testSplits`? In which case there is a lot of
duplication here. I realise it's following the style of the `sampleByKey`
tests, but I wonder if it's worth merging the common set up code?
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