[jira] [Assigned] (SPARK-20604) Allow Imputer to handle all numeric types

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-20604?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen reassigned SPARK-20604:
---------------------------------

    Assignee: Wayne Zhang

> Allow Imputer to handle all numeric types
> -----------------------------------------
>
>                 Key: SPARK-20604
>                 URL: https://issues.apache.org/jira/browse/SPARK-20604
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.1.0
>            Reporter: Wayne Zhang
>            Assignee: Wayne Zhang
>            Priority: Major
>
> Imputer currently requires input column to be Double or Float, but the logic should work on any numeric data types. Many practical problems have integer  data types, and it could get very tedious to manually cast them into Double before calling imputer. This transformer could be extended to handle all numeric types.  
> The example below shows failure of Imputer on integer data. 
> {code}
>     val df = spark.createDataFrame( Seq(
>       (0, 1.0, 1.0, 1.0),
>       (1, 11.0, 11.0, 11.0),
>       (2, 1.5, 1.5, 1.5),
>       (3, Double.NaN, 4.5, 1.5)
>     )).toDF("id", "value1", "expected_mean_value1", "expected_median_value1")
>     val imputer = new Imputer()
>       .setInputCols(Array("value1"))
>       .setOutputCols(Array("out1"))
>     imputer.fit(df.withColumn("value1", col("value1").cast(IntegerType)))
> java.lang.IllegalArgumentException: requirement failed: Column value1 must be of type equal to one of the following types: [DoubleType, FloatType] but was actually of type IntegerType.
> {code}



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