Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8587#discussion_r43162488
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
---
@@ -556,6 +556,33 @@ abstract class AggregationQuerySuite extends QueryTest
with SQLTestUtils with Te
Row(0, null, 1, 1, null, 0) :: Nil)
}
+ test("pearson correlation") {
+ val df = Seq.tabulate(10)(i => (1.0 * i, 2.0 * i, i * -1.0)).toDF("a",
"b", "c")
+ val corr1 = df.repartition(2).groupBy().agg(corr("a",
"b")).collect()(0).getDouble(0)
+ assert(math.abs(corr1 - 1.0) < 1e-12)
+ val corr2 = df.groupBy().agg(corr("a", "c")).collect()(0).getDouble(0)
+ assert(math.abs(corr2 + 1.0) < 1e-12)
+ // non-trivial example. To reproduce in python, use:
+ // >>> from scipy.stats import pearsonr
+ // >>> import numpy as np
+ // >>> a = np.array(range(20))
+ // >>> b = np.array([x * x - 2 * x + 3.5 for x in range(20)])
+ // >>> pearsonr(a, b)
+ // (0.95723391394758572, 3.8902121417802199e-11)
+ // In R, use:
+ // > a <- 0:19
+ // > b <- mapply(function(x) x * x - 2 * x + 3.5, a)
+ // > cor(a, b)
+ // [1] 0.957233913947585835
+ val df2 = Seq.tabulate(20)(x => (1.0 * x, x * x - 2 * x +
3.5)).toDF("a", "b")
+ val corr3 = df2.groupBy().agg(corr("a", "b")).collect()(0).getDouble(0)
+ assert(math.abs(corr3 - 0.95723391394758572) < 1e-12)
+
+ val df3 = Seq.tabulate(0)(i => (1.0 * i, 2.0 * i)).toDF("a", "b")
+ val corr4 = df3.groupBy().agg(corr("a", "b")).collect()(0).getDouble(0)
+ assert(corr4.isNaN)
+ }
--- End diff --
What will happen if the data type of input parameters are not double?
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