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https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15883125#comment-15883125
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Bill Chambers edited comment on SPARK-19714 at 2/24/17 5:15 PM:
The thing is QuantileDiscretizer and Bucketizer do fundamentally different
things so there are different use cases there (quantiles vs actual values).
It's more of a nuisance than anything and an unclear parameter that seems to
imply things that are not actually the case.
Here's where it *really* falls apart, if I have a bucket and I provide one
split, how many buckets do I have?
In Bucketizer I have none! That makes little sense. Splits is not the correct
word here either because they aren't splits! They're bucket boundaries. I think
this is more than a documentation issue, even though those aren't very clear
themselves.
> Parameter for mapping continuous features into buckets. With n+1 splits,
> there are n buckets. A bucket defined by splits x,y holds values in the range
> [x,y) except the last bucket, which also includes y. Splits should be of
> length greater than or equal to 3 and strictly increasing. Values at -inf,
> inf must be explicitly provided to cover all Double values; otherwise, values
> outside the splits specified will be treated as errors.
I also realize I'm being a pain here :) and that this stuff is always
difficult. I empathize with that, it's just that this method doesn't seem to
use correct terminology or a conceptually relevant implementation for what it
aims to do.
was (Author: bill_chambers):
The thing is QuantileDiscretizer and Bucketizer do fundamentally different
things so there are different use cases there (quantiles vs actual values).
It's more of a nuisance than anything and an unclear parameter that seems to
imply things that are not actually the case.
Here's where it *really* falls apart, if I have a bucket and I provide one
split, how many buckets do I have?
In Bucketizer I have none! That makes no sense. Splits is not the correct word
here either because they aren't splits! They're bounds or containers or buckets
themselves. I think this is more than a documentation issue, even though those
aren't very clear themselves.
> Parameter for mapping continuous features into buckets. With n+1 splits,
> there are n buckets. A bucket defined by splits x,y holds values in the range
> [x,y) except the last bucket, which also includes y. Splits should be of
> length greater than or equal to 3 and strictly increasing. Values at -inf,
> inf must be explicitly provided to cover all Double values; otherwise, values
> outside the splits specified will be treated as errors.
> Bucketizer Bug Regarding Handling Unbucketed Inputs
> ---
>
> Key: SPARK-19714
> URL: https://issues.apache.org/jira/browse/SPARK-19714
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
>Affects Versions: 2.1.0
>Reporter: Bill Chambers
>
> {code}
> contDF = spark.range(500).selectExpr("cast(id as double) as id")
> import org.apache.spark.ml.feature.Bucketizer
> val splits = Array(5.0, 10.0, 250.0, 500.0)
> val bucketer = new Bucketizer()
> .setSplits(splits)
> .setInputCol("id")
> .setHandleInvalid("skip")
> bucketer.transform(contDF).show()
> {code}
> You would expect that this would handle the invalid buckets. However it fails
> {code}
> Caused by: org.apache.spark.SparkException: Feature value 0.0 out of
> Bucketizer bounds [5.0, 500.0]. Check your features, or loosen the
> lower/upper bound constraints.
> {code}
> It seems strange that handleInvalud doesn't actually handleInvalid inputs.
> Thoughts anyone?
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