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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-14533) RowMatrix.computeCovariance inaccurate when values are very large
Date Mon, 11 Apr 2016 12:38:25 GMT

     [ https://issues.apache.org/jira/browse/SPARK-14533?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-14533:
------------------------------------

    Assignee: Apache Spark  (was: Sean Owen)

> RowMatrix.computeCovariance inaccurate when values are very large
> -----------------------------------------------------------------
>
>                 Key: SPARK-14533
>                 URL: https://issues.apache.org/jira/browse/SPARK-14533
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.6.1, 2.0.0
>            Reporter: Sean Owen
>            Assignee: Apache Spark
>            Priority: Minor
>
> The following code will produce a Pearson correlation that's quite different from 0,
sometimes outside [-1,1] or even NaN:
> {code}
>     val a = RandomRDDs.normalRDD(sc, 100000, 10).map(_ + 1000000000.0)
>     val b = RandomRDDs.normalRDD(sc, 100000, 10).map(_ + 1000000000.0)
>     val p = Statistics.corr(a, b, method = "pearson")
> {code}
> This is a "known issue" to some degree, given how Cov(X,Y) is calculated in {{RowMatrix.getCovariance}},
as Cov(X,Y) = E[XY] - E[X]E[Y]. The easier and more accurate approach involves just centering
the input before computing the Gramian, but this would be inefficient for sparse data.
> However, for dense data -- which includes the code paths that compute correlations --
this approach is quite sensible. This would improve accuracy for the dense row case, at least.
> Also, the mean column values computed in this method can be computed more simply and
accurately from {{computeColumnSummaryStatistics()}}



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