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https://issues.apache.org/jira/browse/SPARK-30202?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhengruifeng resolved SPARK-30202.
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Resolution: Not A Problem
> 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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