srowen commented on a change in pull request #17864: [SPARK-20604][ML] Allow
imputer to handle numeric types
URL: https://github.com/apache/spark/pull/17864#discussion_r309388602
##########
File path: mllib/src/test/scala/org/apache/spark/ml/feature/ImputerSuite.scala
##########
@@ -176,6 +178,51 @@ class ImputerSuite extends MLTest with
DefaultReadWriteTest {
assert(newInstance.surrogateDF.collect() ===
instance.surrogateDF.collect())
}
+ test("Imputer for Numeric with default missing Value NaN") {
+ val df = spark.createDataFrame(Seq(
+ (0, 1.0, 1.0, 1.0),
+ (1, 11.0, 11.0, 11.0),
+ (2, 3.6, 3.6, 3.6),
+ (3, Double.NaN, 5.2, 3.6)
+ )).toDF("id", "value1", "expected_mean_value1_double",
"expected_median_value1_double")
+
+ val imputer = new Imputer()
+ .setInputCols(Array("value1"))
+ .setOutputCols(Array("out1"))
+
+ val types = Seq(ShortType, IntegerType, LongType, FloatType, DoubleType,
Review comment:
I think this is fine, but wouldn't necessarily bother with anything but
Integer Long, maybe Float. (Not sure how long the test takes to run.)
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