Frank Dai created SPARK-17400:
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Summary: MinMaxScaler.transform() outputs DenseVector by default,
which causes poor performance
Key: SPARK-17400
URL: https://issues.apache.org/jira/browse/SPARK-17400
Project: Spark
Issue Type: Improvement
Components: ML, MLlib
Affects Versions: 2.0.0, 1.6.2, 1.6.1
Reporter: Frank Dai
MinMaxScaler.transform() outputs DenseVector by default, which will cause poor
performance and consume a lot of memory.
The most important line of code is the following:
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala#L195
I suggest that the code should calculate the number of non-zero elements in
advance, if the number of non-zero elements is less than half of the total
elements in the matrix, use SparseVector, otherwise use DenseVector
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