Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10355#discussion_r47933061
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -275,6 +274,40 @@ class DecisionTreeClassifierSuite extends
SparkFunSuite with MLlibTestSparkConte
val model = dt.fit(df)
}
+ test("DecisionTree should support all NumericType labels") {
+ val dfWithIntLabels =
TreeTests.setMetadata(sqlContext.createDataFrame(Seq(
--- End diff --
It might be less verbose to create the dataframe once, and then add the
other label column types to the same data frame. Something like:
```scala
val dfWithTypes = df
.withColumn("shortLabel", df("labelIndex").cast(ShortType))
.withColumn("longLabel", df("labelIndex").cast(LongType))
.withColumn("intLabel", df("labelIndex").cast(IntegerType))
.withColumn("floatLabel", df("labelIndex").cast(FloatType))
.withColumn("decimalLabel", df("labelIndex").cast(DecimalType(10, 0)))
```
Then just change the label column between training. I'm not sure which way
is better, but this would reduce copying the code ~5 times per test.
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