Joseph K. Bradley updated SPARK-17219:
    Affects Version/s:     (was: 2.0.1)
                           (was: 2.1.0)
                           (was: 1.6.2)
     Target Version/s: 2.1.0

> QuantileDiscretizer should handle NaN values gracefully
> -------------------------------------------------------
>                 Key: SPARK-17219
>                 URL: https://issues.apache.org/jira/browse/SPARK-17219
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: Barry Becker
>            Assignee: Vincent
> How is the QuantileDiscretizer supposed to handle null values?
> Actual nulls are not allowed, so I replace them with Double.NaN.
> However, when you try to run the QuantileDiscretizer on a column that 
> contains NaNs, it will create (possibly more than one) NaN split(s) before 
> the final PositiveInfinity value.
> I am using the attache titanic csv data and trying to bin the "age" column 
> using the QuantileDiscretizer with 10 bins specified. The age column as a lot 
> of null values.
> These are the splits that I get:
> {code}
> -Infinity, 15.0, 20.5, 24.0, 28.0, 32.5, 38.0, 48.0, NaN, NaN, Infinity
> {code}
> Is that expected. It seems to imply that NaN is larger than any positive 
> number and less than infinity.
> I'm not sure of the best way to handle nulls, but I think they need a bucket 
> all their own. My suggestions would be to include an initial NaN split value 
> that is always there, just like the sentinel Infinities are. If that were the 
> case, then the splits for the example above might look like this:
> {code}
> NaN, -Infinity, 15.0, 20.5, 24.0, 28.0, 32.5, 38.0, 48.0, Infinity
> {code}
> This does not seem great either because a bucket that is [NaN, -Inf] doesn't 
> make much sense. Not sure if the NaN bucket counts toward numBins or not. I 
> do think it should always be there though in case future data has null even 
> though the fit data did not. Thoughts?

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