Weichen Xu created SPARK-16561:
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Summary: 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
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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