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Posted to issues@spark.apache.org by "Weichen Xu (JIRA)" <ji...@apache.org> on 2016/07/15 04:32:20 UTC
[jira] [Updated] (SPARK-16561) Potential numerial problem in
MultivariateOnlineSummarizer min/max
[ https://issues.apache.org/jira/browse/SPARK-16561?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Weichen Xu updated SPARK-16561:
-------------------------------
Description:
In `MultivariateOnlineSummarizer` min/max method,
use judgement nnz(i) < weightSum, it will cause some numerial problem
and make result unstable.
for example,
add two vector:
[10, -10] with weight 1e10
[0, 0] with weight 1e-10
using MultivariateOnlineSummarizer.min/max we will get
minVector = [10, -10]
maxVector = [10, -10]
but the right result should be
minVector = [0, -10]
maxVector = [10, 0]
The bug reason is that
(1e10 + 1e-10) == 1e10 because of the floating rounding.
and different accumulating or merging order may cause different result,
such as:
[10, -10] with weight 1e10
[0, 0] with weight 1e-7
....
(100 lines data [0, 0] with weight 1e-7)
using the input data order listed above, we will get the result:
minVector = [10, -10]
maxVector = [10, -10]
but if the input data order is as following:
[0, 0] with weight 1e-7
....
(100 lines data [0, 0] with weight 1e-7)
[10, -10] with weight 1e10
than it the result will be:
minVector = [0, -10]
maxVector = [10, 0]
that's because:
1e10 + 1e-7 + ... + 1e-7(add 100 times) == 1e10
but
1e-7 + ... + 1e-7(add 100 times) + 1e10 = 1.000000000000001E10 != 1e10
was:
In `MultivariateOnlineSummarizer` min/max method,
use judgement `nnz(i) < weightSum`, it will cause some numerial problem
and make result unstable.
for example,
add two vector:
[10, -10] with weight 1e10
[0, 0] with weight 1e-10
using MultivariateOnlineSummarizer.min/max we will get
minVector = [10, -10]
maxVector = [10, -10]
but the right result should be
minVector = [0, -10]
maxVector = [10, 0]
The bug reason is that
(1e10 + 1e-10) == 1e10 because of the floating rounding.
and different accumulating or merging order may cause different result,
such as:
[10, -10] with weight 1e10
[0, 0] with weight 1e-7
....
(100 lines data [0, 0] with weight 1e-7)
using the input data order listed above, we will get the result:
minVector = [10, -10]
maxVector = [10, -10]
but if the input data order is as following:
[0, 0] with weight 1e-7
....
(100 lines data [0, 0] with weight 1e-7)
[10, -10] with weight 1e10
than it the result will be:
minVector = [0, -10]
maxVector = [10, 0]
that's because:
1e10 + 1e-7 + ... + 1e-7(add 100 times) == 1e10
but
1e-7 + ... + 1e-7(add 100 times) + 1e10 = 1.000000000000001E10 != 1e10
> Potential numerial problem in MultivariateOnlineSummarizer min/max
> ------------------------------------------------------------------
>
> Key: SPARK-16561
> URL: https://issues.apache.org/jira/browse/SPARK-16561
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 2.0.0, 2.0.1, 2.1.0
> Reporter: Weichen Xu
> Assignee: Apache Spark
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> In `MultivariateOnlineSummarizer` min/max method,
> use judgement nnz(i) < weightSum, it will cause some numerial problem
> and make result unstable.
> for example,
> add two vector:
> [10, -10] with weight 1e10
> [0, 0] with weight 1e-10
> using MultivariateOnlineSummarizer.min/max we will get
> minVector = [10, -10]
> maxVector = [10, -10]
> but the right result should be
> minVector = [0, -10]
> maxVector = [10, 0]
> The bug reason is that
> (1e10 + 1e-10) == 1e10 because of the floating rounding.
> and different accumulating or merging order may cause different result,
> such as:
> [10, -10] with weight 1e10
> [0, 0] with weight 1e-7
> ....
> (100 lines data [0, 0] with weight 1e-7)
> using the input data order listed above, we will get the result:
> minVector = [10, -10]
> maxVector = [10, -10]
> but if the input data order is as following:
> [0, 0] with weight 1e-7
> ....
> (100 lines data [0, 0] with weight 1e-7)
> [10, -10] with weight 1e10
> than it the result will be:
> minVector = [0, -10]
> maxVector = [10, 0]
> that's because:
> 1e10 + 1e-7 + ... + 1e-7(add 100 times) == 1e10
> but
> 1e-7 + ... + 1e-7(add 100 times) + 1e10 = 1.000000000000001E10 != 1e10
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