[GitHub] [spark] zhengruifeng edited a comment on issue #27978: [SPARK-31127][ML] Implement abstract Selector

[GitBox <gi...@apache.org>]

zhengruifeng edited a comment on issue #27978: [SPARK-31127][ML] Implement abstract Selector
URL: https://github.com/apache/spark/pull/27978#issuecomment-604859600
 
 
   What about this plan?
   1, adding a method to select features by `SelectionResult` in `object SelectionTestResult`:
   ```scala
   private[ml] object SelectionTestResult {
       def select(results: Array[SelectionTestResult], selectorType: String, other params: ...): Array[Int] (or Array[(SelectionTestResult, Int)])
   }
   ```
   
   2, moving transformation function into `object ChiSqSelectorModel`, and then reusing it in other classes
   ```scala
       val newSize = selectedFeatures.length
       val func = { vector: Vector =>
         vector match {
           case SparseVector(_, indices, values) =>
             val (newIndices, newValues) = compressSparse(indices, values)
             Vectors.sparse(newSize, newIndices, newValues)
           case DenseVector(values) =>
             Vectors.dense(selectedFeatures.map(values))
           case other =>
             throw new UnsupportedOperationException(
               s"Only sparse and dense vectors are supported but got ${other.getClass}.")
         }
       }
   
   ```
   
   In this way, there will be no breaking change

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