mengxr commented on a change in pull request #27522: [SPARK-30762] Add
dtype=float32 support to vector_to_array UDF
URL: https://github.com/apache/spark/pull/27522#discussion_r378435400
##########
File path: mllib/src/test/scala/org/apache/spark/ml/FunctionsSuite.scala
##########
@@ -61,5 +59,34 @@ class FunctionsSuite extends MLTest {
"`org.apache.spark.ml.linalg.Vector` or
`org.apache.spark.mllib.linalg.Vector`, " +
s"but got ${valType}"))
}
+
+ val df3 = Seq(
+ (Vectors.dense(1.0, 2.0, 3.0), OldVectors.dense(10.0, 20.0, 30.0)),
+ (Vectors.sparse(3, Seq((0, 2.0), (2, 3.0))), OldVectors.sparse(3,
Seq((0, 20.0), (2, 30.0))))
+ ).toDF("vec", "oldVec")
+ val df_array_float = df3.select(
+ vector_to_array('vec, dtype = "float32"), vector_to_array('oldVec, dtype
= "float32"))
+
+ // Check values are correct
+ val result3 = df_array_float.as[(Seq[Float], Seq[Float])].collect().toSeq
+
+ val expected3 = Seq(
+ (Seq(1.0, 2.0, 3.0), Seq(10.0, 20.0, 30.0)),
+ (Seq(2.0, 0.0, 3.0), Seq(20.0, 0.0, 30.0))
+ )
+ assert(result3 === expected3)
+
+ // Check data types are correct
+ df_array_double.schema.fields(0).dataType.simpleString == "array<double>"
Review comment:
* there is no check here
* you can assert on the simple string of the entire schema directly:
~~~
assert(df.schema.simpleString === "...")
~~~
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