Xinyong Tian created SPARK-26166:
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Summary: CrossValidator.fit() bug,training and validation dataset
may overlap
Key: SPARK-26166
URL: https://issues.apache.org/jira/browse/SPARK-26166
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.3.0
Reporter: Xinyong Tian
In the code pyspark.ml.tuning.CrossValidator.fit(), after adding random column
df = dataset.select("*", rand(seed).alias(randCol))
Should add
df.cache()
If df not cached, it will be reselect each time when train and validation
dataframe need to be created. The order of rows in df,which rand(seed) is
dependent on, is not deterministic . Thus each time random column value could
be different for a specific row even with seed.
This might especially be a problem when input 'dataset' dataframe is resulted
from a query including 'where' clause. see below.
https://dzone.com/articles/non-deterministic-order-for-select-with-limit
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