[spark] branch master updated: [SPARK-31671][ML] Wrong error message in VectorAssembler

[sr...@apache.org]

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The following commit(s) were added to refs/heads/master by this push:
     new 64fb358  [SPARK-31671][ML] Wrong error message in VectorAssembler
64fb358 is described below

commit 64fb358a994d3fff651a742fa067c194b7455853
Author: fan31415 <fa...@gmail.com>
AuthorDate: Mon May 11 18:23:23 2020 -0500

    [SPARK-31671][ML] Wrong error message in VectorAssembler
    
    ### What changes were proposed in this pull request?
    When input column lengths can not be inferred and handleInvalid = "keep",  VectorAssembler will throw a runtime exception. However the error message with this exception is not consistent. I change the content of this error message to make it work properly.
    
    ### Why are the changes needed?
    This is a bug. Here is a simple example to reproduce it.
    
    ```
    // create a df without vector size
    val df = Seq(
      (Vectors.dense(1.0), Vectors.dense(2.0))
    ).toDF("n1", "n2")
    
    // only set vector size hint for n1 column
    val hintedDf = new VectorSizeHint()
      .setInputCol("n1")
      .setSize(1)
      .transform(df)
    
    // assemble n1, n2
    val output = new VectorAssembler()
      .setInputCols(Array("n1", "n2"))
      .setOutputCol("features")
      .setHandleInvalid("keep")
      .transform(hintedDf)
    
    // because only n1 has vector size, the error message should tell us to set vector size for n2 too
    output.show()
    ```
    
    Expected error message:
    
    ```
    Can not infer column lengths with handleInvalid = "keep". Consider using VectorSizeHint to add metadata for columns: [n2].
    ```
    
    Actual error message:
    
    ```
    Can not infer column lengths with handleInvalid = "keep". Consider using VectorSizeHint to add metadata for columns: [n1, n2].
    ```
    
    This introduce difficulties when I try to resolve this exception, for I do not know which column required vectorSizeHint. This is especially troublesome when you have a large number of columns to deal with.
    
    ### Does this PR introduce _any_ user-facing change?
    No.
    
    ### How was this patch tested?
    Add test in VectorAssemblerSuite.
    
    Closes #28487 from fan31415/SPARK-31671.
    
    Lead-authored-by: fan31415 <fa...@gmail.com>
    Co-authored-by: yijiefan <fa...@gmail.com>
    Signed-off-by: Sean Owen <sr...@gmail.com>
---
 .../scala/org/apache/spark/ml/feature/VectorAssembler.scala   |  2 +-
 .../org/apache/spark/ml/feature/VectorAssemblerSuite.scala    | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 3070012..7bc5e56 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -233,7 +233,7 @@ object VectorAssembler extends DefaultParamsReadable[VectorAssembler] {
         getVectorLengthsFromFirstRow(dataset.na.drop(missingColumns), missingColumns)
       case (true, VectorAssembler.KEEP_INVALID) => throw new RuntimeException(
         s"""Can not infer column lengths with handleInvalid = "keep". Consider using VectorSizeHint
-           |to add metadata for columns: ${columns.mkString("[", ", ", "]")}."""
+           |to add metadata for columns: ${missingColumns.mkString("[", ", ", "]")}."""
           .stripMargin.replaceAll("\n", " "))
       case (_, _) => Map.empty
     }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
index a4d388f..4957f6f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
@@ -261,4 +261,15 @@ class VectorAssemblerSuite
     val output = vectorAssembler.transform(dfWithNullsAndNaNs)
     assert(output.select("a").limit(1).collect().head == Row(Vectors.sparse(0, Seq.empty)))
   }
+
+  test("SPARK-31671: should give explicit error message when can not infer column lengths") {
+    val df = Seq(
+      (Vectors.dense(1.0), Vectors.dense(2.0))
+    ).toDF("n1", "n2")
+    val hintedDf = new VectorSizeHint().setInputCol("n1").setSize(1).transform(df)
+    val assembler = new VectorAssembler()
+      .setInputCols(Array("n1", "n2")).setOutputCol("features")
+    assert(!intercept[RuntimeException](assembler.setHandleInvalid("keep").transform(hintedDf))
+      .getMessage.contains("n1"), "should only show no vector size columns' name")
+  }
 }


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