[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15564828#comment-15564828
]
Sean Owen commented on SPARK-17219:
---
Yeah, unless you return some complex object with normal buckets and a specially
separated other bucket, then returning all the buckets together implies some
kind of ordering, even if you need not _necessarily_ think of buckets as
ordered. They're just counts. It does open this up to misinterpretation from
careless callers.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15564786#comment-15564786
]
Apache Spark commented on SPARK-17219:
--
User 'VinceShieh' has created a pull request for this issue:
https://github.com/apache/spark/pull/15428
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15563247#comment-15563247
]
Joseph K. Bradley commented on SPARK-17219:
---
True, there is an order created there, but the order is not meaningful. That
is, NaN is not "bigger than" all other values. Imposing this arbitrary order
may make some algorithms treat the data incorrectly.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15559597#comment-15559597
]
Vincent commented on SPARK-17219:
-
No problem. I will try to submit another PR based on above discussions.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15557755#comment-15557755
]
Sean Owen commented on SPARK-17219:
---
@Vincent thanks for your perseverance. I know we discussed this at length, but,
I'd defer to Timothy/Joseph's preference here. If you're willing, you can open
another PR to simply undo some of the changes and instead reject NaN input
entirely. If not, I can do it next week.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15557034#comment-15557034
]
Vincent commented on SPARK-17219:
-
[~josephkb] [~srowen] [~timhunter] let me know what I can do to help if there
is anything.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15557021#comment-15557021
]
Vincent commented on SPARK-17219:
-
in this PR(https://github.com/apache/spark/pull/14858) NaN values are always
put into the last bucket.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15556130#comment-15556130
]
Joseph K. Bradley commented on SPARK-17219:
---
That does make sense, and I agree we should improve handling of nulls/NaNs. We
could extend (b) above by specializing {{HasHandleInvalid}} (maybe using a
different Param name) to allow the option you implemented, where NaN values get
a separate bucket. I'd be happy with that too, though it does have one
complication: Buckets are naturally ordered, but a NaN bucket would not belong
in this order.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=1968#comment-1968
]
Barry Becker commented on SPARK-17219:
--
I'll make another attempt to clarify my use case.
Nulls are different than normal values, and trying to impute them changes the
data and its interpretation - possibly in a misleading way.
Throwing out records with null values (or giving an error on them) is worse
because you are discarding a lot of potentially useful information.
Suppose you have survey results or exam data results. If you try to impute the
answer that a student should have made on the exam before you do your ML, you
will get results that make it look like all students answered all questions,
when it might have been the case that many were left blank. Similar situation
for survey data. The fact that responses were left blank is important. You
don't want to discard it or replace it with some actual value if it was left
blank.
I wish spark would handle nulls as first class entities throughout MLlib.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=1925#comment-1925
]
Sean Owen commented on SPARK-17219:
---
OK. Let me just work up a patch to fix forward rather than bother with
reverting.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=1883#comment-1883
]
Joseph K. Bradley commented on SPARK-17219:
---
I agree with [~thunterdb]. The 2 ways of handling invalid values in MLlib
currently are either (a) throw an error or (b) provide an option to skip rows
with invalid data (via shared param {{HasHandleInvalid}}, which is only used by
StringIndexer but could be used elsewhere too). Could we revert this patch and
do a new one? Thanks!
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15553503#comment-15553503
]
Sean Owen commented on SPARK-17219:
---
I could go this way too. I ended up sympathizing with trying to return a result
rather than refusing. to the extent NaN is a corner case, then, the behavior in
this corner case isn't going to impact mainstream usage. And you can ignore /
reject NaN before calling this if you want. Stare decisis, I figure, unless
anyone feels strongly about it. It's only in master at this point, and
unreleased.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15553490#comment-15553490
]
Timothy Hunter commented on SPARK-17219:
If I understand correctly the PR, I am concerned by this approach for a couple
of reasons:
- when users set the number of buckets, the general expectation should be that
(number of returned buckets) <= (number of requested buckets). With the current
treatment of NaN, you can end up with more buckets than you asked for. Breaking
this invariant seems troublesome for me.
- in general, MLLib's policy in regard to NaNs has been to consider them as
invalid input. This is also the approach followed by sklearn and the reason for
having an imputer with SPARK-13568. If we start to let NaN values go through,
they will trigger some other issues down the pipelines.
Why not simply stopping with an error at that point, as [~srowen] was
suggesting at the beginning? [~barrybecker4], I am trying to understand your
use case here.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>Assignee: Vincent
> Fix For: 2.1.0
>
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445873#comment-15445873
]
Vincent commented on SPARK-17219:
-
Cool. I will refine the patch. thanks [~srowen] :)
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445862#comment-15445862
]
Sean Owen commented on SPARK-17219:
---
Agree, and that's a reasonable requirement for any implementation.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445858#comment-15445858
]
Vincent commented on SPARK-17219:
-
yes, discretizer can do it easily, especially if only QuantileDiscretizer is in
question. But same changes should also be applied to other discretizers in the
future, like, as Berry mentioned, MDLPDiscretizer, etc.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445820#comment-15445820
]
Sean Owen commented on SPARK-17219:
---
No, the discretizer can do this easily, right? The discretizer need never
return splits with a NaN. But the bucketizer can still handle NaN because the
behavior is clear: goes to a special bucket. It has nothing to do with splits.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445808#comment-15445808
]
Vincent commented on SPARK-17219:
-
then we have to shift this work to user, who needs to filter out the NaN value
if, somehow they got the NaN in their split from functions such as quantile.
And we will make a check before setSplits to Bucketizer, throwing an error if
NaN split found, while put NaN from data input into an extra bucket in
Bucketizer.transform. Sounds good?
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445779#comment-15445779
]
Sean Owen commented on SPARK-17219:
---
No, there's no meaning to a split bounded by NaN. However, it's possible
(unfortunately) that input can contain NaN. These values do not affect the
splits derived from the input because they have no ordering. They can't go into
any buckets defined by splits because they have no ordering. They go into a
special bucket.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445768#comment-15445768
]
Vincent commented on SPARK-17219:
-
[~srowen] Hi all, per discussion, I thought we are going to handle NaN splits,
I mean to allow NaN exist in splits, rather than rejecting - although NaN
splits dont really make sense, I think one pro to process NaN splits is making
bucketizer more robust. If instead, we reject the NaN splits, it'd be better we
give an error msg to user that, such error is due to NaN value found in the
splits, in terms of complexity, rejecting or accepting NaN splits, they cost
the same.
what do you think?
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15445198#comment-15445198
]
Apache Spark commented on SPARK-17219:
--
User 'VinceShieh' has created a pull request for this issue:
https://github.com/apache/spark/pull/14858
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436873#comment-15436873
]
Vincent commented on SPARK-17219:
-
Okay, thanks.
So, meaning we will have no options for users actually. We will put NaN in an
extra bucket. I think we should document it, in case users might find it
confusing, they should be informed to handle extra bucket with NaN.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436870#comment-15436870
]
Vincent commented on SPARK-17219:
-
yes, if we wanna make this scenario more general to all bucketizer cases, I
guess you should change the title. Currently the Bucketizer usage is limited,
it wont have a big impact on current code base if we make some changes to the
API.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436863#comment-15436863
]
Sean Owen commented on SPARK-17219:
---
Yes, agree with that. I think it will involve a change to anything that
bucket-izes, and anything that consumes the buckets, because it will require
special handling to put NaN in the right 'bucket'.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436856#comment-15436856
]
Vincent commented on SPARK-17219:
-
[~srowen] sorryOwen, by saying 'keep it to one behavior'? do u mean we just
make extra one bucket whenever Bucketizer find NaN in a cutpoits vector? and
let users handle NaN elements removal/error handling before feeding data to
Bucketizer?
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436819#comment-15436819
]
Barry Becker commented on SPARK-17219:
--
In my opinion, yes. It is something that applies to all bucketizers. Maybe that
means a different jira or changing the title of this one.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436804#comment-15436804
]
Vincent commented on SPARK-17219:
-
if so, we have to add this option within Bucketizer, right?
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436776#comment-15436776
]
Sean Owen commented on SPARK-17219:
---
Let's keep it to one behavior, an extra bucket. The caller can remove NaNs if
desired, or produce an error, if really desired.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436767#comment-15436767
]
Barry Becker commented on SPARK-17219:
--
If you support the different strategies as R does, please support the option to
have NaN in a separate bucket in addition to ignore or error.
If I am using cutpoints from MDLPDiscretizer to feed to a regular Bucketizer,
it would be inconvenient to have a different code path for the cases where I
wanted just one [-Inf, Inf] and null bins.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436553#comment-15436553
]
Vincent commented on SPARK-17219:
-
I can work on this issue if no one else is on it :)
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436143#comment-15436143
]
Vincent commented on SPARK-17219:
-
for this scenario, we can add a new parameter for QuantileDiscretizer, a
nullStrategy param as Berry mentioned. Actually, R supports such kind of option
by having a "na.rm" flag for user to either remove NaN elements before
quantile, or throw an error (by default). So, I think it's a nice thing to have
in Spark too.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15436136#comment-15436136
]
Vincent commented on SPARK-17219:
-
for cases where only null and non-null buckets are needed, I guess we dont need
to call QuantileDiscretizer to do that
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435651#comment-15435651
]
Barry Becker commented on SPARK-17219:
--
If the decision is to have an additional null/NaN bucket, then I agree that
other choices aren't needed.
I agree that that the null/NaN bucket can be separate from maxBins (i.e.
request 10, but get 11).
A couple of other things to consider:
- I think there should always be a null/NaN bucket present for the same reason
that the first and last bins are -Inf and +Inf respectively. Just because there
were no nulls in the training/fitting data does not mean that they will not
come through later and need to be placed somewhere.
- Currently validation fails if there are fewer than 3 splits specified for a
Bucketizer. I actually think that 2 splits should be the minimum - even though
that means only 1 bucket! The reason is that some algorithms (like Naive Bayes)
may choose to bin features (using MDLP discretization for example) into just 2
buckets - null and non-null. If we now have a null bucket always present, we
may just want a single [-Inf, Inf] bucket to for non-nulls - as strange at that
sounds.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435588#comment-15435588
]
Sean Owen commented on SPARK-17219:
---
Yes, those seem like the 3 options. Hm, I'm reluctant to introduce another set
of choices here and would favor just being opinionated on this point.
How about establishing an additional bucket? the question I suppose is how that
relates to the requested number of buckets. I think it would be in addition.
So requesting 10 buckets means potentially 11 come back.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435484#comment-15435484
]
Barry Becker commented on SPARK-17219:
--
Nulls were not accepted in the column. I had to change them from null to NaN so
they would be. If there were a way keep them as nulls and have the
discretization work, that would be preferable. I think there should be some
strategy for dealing with null (or NaN values) besides just ignoring them. They
are legitimate values in many cases. They represent missing data - which may be
very important and useful for some algorithms. How about having the ability to
set a nullStrategy param that indicates what you want to do with them. For
example, throw an error, ignore them, put them in a special bucket, etc.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435469#comment-15435469
]
Sean Owen commented on SPARK-17219:
---
These aren't null though, but NaN. There's no meaningful way to put them into a
bucket because the value (and null) has no ordering. I suppose it's valid to
ignore them too.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435403#comment-15435403
]
Barry Becker commented on SPARK-17219:
--
There needs to be some way to handle null values when binning.
Maybe they aren't part of the splits, but there should be some recommended best
practice for handling nulls besides just trying to infer non-null values for
them.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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[jira] [Commented] (SPARK-17219) QuantileDiscretizer does strange things with NaN values
[
https://issues.apache.org/jira/browse/SPARK-17219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15435391#comment-15435391
]
Sean Owen commented on SPARK-17219:
---
Ah this is because NaN != NaN. Where it ends up is pretty arbitrary as a
result. It has no ordering with other doubles.
This should probably be rejected as input and raise an error.
> QuantileDiscretizer does strange things with NaN values
> ---
>
> Key: SPARK-17219
> URL: https://issues.apache.org/jira/browse/SPARK-17219
> Project: Spark
> Issue Type: Bug
> Components: ML
>Affects Versions: 1.6.2
>Reporter: Barry Becker
>
> 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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