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https://issues.apache.org/jira/browse/SPARK-39664?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-39664:
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Component/s: ML
(was: Pandas API on Spark)
> RowMatrix(...).computeCovariance() VS Correlation.corr(..., ...)
> ----------------------------------------------------------------
>
> Key: SPARK-39664
> URL: https://issues.apache.org/jira/browse/SPARK-39664
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 3.2.1
> Reporter: igal l
> Priority: Major
>
> I have a Pyspark DF with one column. This column type is Vector and the
> values are DenseVectors of size 768. The DF has 1 million rows.
> I want to calculate the Covariance matrix of this set of vectors.
> When I try to calculate it with
> `RowMatrix(df.rdd.map(list)).computeCovariance()`, it takes 1.57 minuts.
> When I try to calculate the Correlation matrix with `Correlation.corr(df,
> '_1')`, it takes 33 seconds.
> Covariance and Correlation's formula are pretty much the same, therefore, I
> don't understand the gap between them
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