zhengruifeng created SPARK-30202:
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Summary: impl QuantileTransform
Key: SPARK-30202
URL: https://issues.apache.org/jira/browse/SPARK-30202
Project: Spark
Issue Type: Improvement
Components: ML, PySpark
Affects Versions: 3.0.0
Reporter: zhengruifeng
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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