Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/19156#discussion_r156564056 --- Diff: mllib/src/test/scala/org/apache/spark/ml/stat/SummarizerSuite.scala --- @@ -205,67 +207,21 @@ class SummarizerSuite extends SparkFunSuite with MLlibTestSparkContext { } } - test("debugging test") { - val df = denseData(Nil) - val c = df.col("features") - val c1 = metrics("mean").summary(c) - val res = df.select(c1) - intercept[SparkException] { - compare(res, Seq.empty) - } - } - - test("basic error handling") { - val df = denseData(Nil) - val c = df.col("features") - val res = df.select(metrics("mean").summary(c), mean(c)) - intercept[SparkException] { - compare(res, Seq.empty) - } - } + testExample("single element", Seq((Vectors.dense(0.0, 1.0, 2.0), 2.0))) - test("no element, working metrics") { - val df = denseData(Nil) - val c = df.col("features") - val res = df.select(metrics("count").summary(c), count(c)) - compare(res, Seq(Row(0L), 0L)) - } + testExample("multiple elements (dense)", + Seq( + (Vectors.dense(-1.0, 0.0, 6.0), 0.5), + (Vectors.dense(3.0, -3.0, 0.0), 2.8), + (Vectors.dense(1.0, -3.0, 0.0), 0.0) + ) + ) - val singleElem = Seq(0.0, 1.0, 2.0) - testExample("single element", Seq(singleElem), ExpectedMetrics( - mean = singleElem, - variance = Seq(0.0, 0.0, 0.0), - count = 1, - numNonZeros = Seq(0, 1, 1), - max = singleElem, - min = singleElem, - normL1 = singleElem, - normL2 = singleElem - )) - - testExample("two elements", Seq(Seq(0.0, 1.0, 2.0), Seq(0.0, -1.0, -2.0)), ExpectedMetrics( - mean = Seq(0.0, 0.0, 0.0), - // TODO: I have a doubt about these values, they are not normalized. - variance = Seq(0.0, 2.0, 8.0), - count = 2, - numNonZeros = Seq(0, 2, 2), - max = Seq(0.0, 1.0, 2.0), - min = Seq(0.0, -1.0, -2.0), - normL1 = Seq(0.0, 2.0, 4.0), - normL2 = Seq(0.0, math.sqrt(2.0), math.sqrt(2.0) * 2.0) - )) - - testExample("dense vector input", - Seq(Seq(-1.0, 0.0, 6.0), Seq(3.0, -3.0, 0.0)), --- End diff -- Why do you remove the test against ground true value?
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