[jira] [Updated] (SPARK-30202) impl QuantileTransform

["Dongjoon Hyun (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-30202?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dongjoon Hyun updated SPARK-30202:
----------------------------------
    Affects Version/s:     (was: 3.0.0)
                       3.1.0

> impl QuantileTransform
> ----------------------
>
>                 Key: SPARK-30202
>                 URL: https://issues.apache.org/jira/browse/SPARK-30202
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>    Affects Versions: 3.1.0
>            Reporter: zhengruifeng
>            Priority: Minor
>
> Recently, I encountered some practice senarinos to map the data to another distribution.
> Then I found that QuantileTransformer in sklearn is what I needed, I locally fitted a model on sampled dataset and broadcast it to transform the whole dataset in pyspark.
> After that I impled QuantileTransform as a new Estimator atop Spark, the impl followed scikit-learn' s impl, however there still are sereral differences:
> 1, use QuantileSummaries for approximation, no matter the size of dataset;
> 2, use linear interpolate, the logic is similar to existing IsotonicRegression, while scikit-learn use a bi-directional interpolate;
> 3, when skipZero=true, treat sparse vectors just like dense ones, while scikit-learn have two different logics for sparse and dense datasets.



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