[jira] [Created] (SPARK-39664) RowMatrix(...).computeCovariance() VS Correlation.corr(..., ...)

["igal l (Jira)" <ji...@apache.org>]

igal l created SPARK-39664:
------------------------------

             Summary: RowMatrix(...).computeCovariance() VS Correlation.corr(..., ...)
                 Key: SPARK-39664
                 URL: https://issues.apache.org/jira/browse/SPARK-39664
             Project: Spark
          Issue Type: Bug
          Components: Pandas API on Spark, PySpark
    Affects Versions: 3.2.1
            Reporter: igal l


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



--
This message was sent by Atlassian Jira
(v8.20.10#820010)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org