Wayne Zhang created SPARK-20574:
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Summary: Allow Bucketizer to handle non-Double column
Key: SPARK-20574
URL: https://issues.apache.org/jira/browse/SPARK-20574
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.1.0
Reporter: Wayne Zhang
Bucketizer currently requires input column to be Double, but the logic should
work on any numeric data types. Many practical problems have integer/float data
types, and it could get very tedious to manually cast them into Double before
calling bucketizer. This transformer could be extended to handle all numeric
types.
The example below shows failure of Bucketizer on integer data.
{code}
val splits = Array(-3.0, 0.0, 3.0)
val data: Array[Int] = Array(-2, -1, 0, 1, 2)
val expectedBuckets = Array(0.0, 0.0, 1.0, 1.0, 1.0)
val dataFrame = data.zip(expectedBuckets).toSeq.toDF("feature", "expected")
val bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
bucketizer.transform(dataFrame)
java.lang.IllegalArgumentException: requirement failed: Column feature must be
of type DoubleType but was actually IntegerType.
{code}
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